def _Preprocess2(code, img, ker):
stpwch = time.time()
VIS._ProgressBar(0,
20,
prefix='(%s):\t%8.3f sec' % (code, time.time() - stpwch),
suffix='Initializing...')
## Initialization ##
sz = np.shape(img)[2:] # Get the true size of the image #
# Inflated size #
sz_off = int(
np.ceil(2 * np.max([USER.PRE_BG, USER.PRE_NS, USER.PRE_LT]) /
USER.RES[0])) # 2 sigma #
sz_ = (sz[0] + 2 * sz_off, sz[1] + 2 * sz_off)
# Construct filters for both sizes - ker will use the original sizes #
h_bg, h_ns, h_lt = _GetFilters(sz)
h_bg_, h_ns_, h_lt_ = _GetFilters(sz_)
# Place the image in the inflated array #
im = np.zeros([*np.shape(img)[:2], *sz_])
im[:, :, sz_off:-sz_off, :][:, :, :, sz_off:-sz_off] = img
# Smooth out the edges #
for i in range(sz_off + 1):
fwd = (np.sin((np.pi / 2) * (i / sz_off)) + 1) / 2
rwd = 1 - fwd
im[:, :,
i, :] = im[:, :, sz_off, :] * fwd + im[:, :, -sz_off - 2, :] * rwd
im[:, :, :,
i] = im[:, :, :, sz_off] * fwd + im[:, :, :, -sz_off - 2] * rwd
im[:, :, -i -
1, :] = im[:, :, sz_off, :] * rwd + im[:, :, -sz_off - 2, :] * fwd
im[:, :, :,
-i - 1] = im[:, :, :, sz_off] * rwd + im[:, :, :, -sz_off - 2] * fwd
## Filtering ##
# Kernel #
VIS._ProgressBar(1,
20,
prefix='(%s):\t%8.3f sec' % (code, time.time() - stpwch),
suffix='Filtering Kernel...')
ker_bg = ker - _ApplyFilter(ker, h_bg)
ker_ns = _ApplyFilter(ker_bg, h_ns)
ker_ = ker_ns * np.ptp(ker) / np.ptp(ker_ns) # Renormalize #
# Inflated Image #
VIS._ProgressBar(2,
20,
prefix='(%s):\t%8.3f sec' % (code, time.time() - stpwch),
suffix='BG Subtraction... ')
img_bg = im - _ApplyFilter(im, h_bg_)
VIS._ProgressBar(7,
20,
prefix='(%s):\t%8.3f sec' % (code, time.time() - stpwch),
suffix='Noise Suppression...')
img_ns = _ApplyFilter(img_bg, h_ns_)
VIS._ProgressBar(12,
20,
prefix='(%s):\t%8.3f sec' % (code, time.time() - stpwch),
suffix='Temporal Smoothing...')
if ((np.shape(img)[0] > USER.PRE_TS // 10 + 1) and (USER.PRE_TS > 0)):
for f in range(np.shape(img)[0]): # Temporal smoothing #
lft = np.maximum(0, f - USER.PRE_TS // 2)
rgt = np.minimum(f + USER.PRE_TS // 2, np.shape(img)[0])
img_ts = np.mean(img_ns[lft:rgt, ...], axis=0)
img_ns[f] = img_ns[f] - img_ts * np.ptp(img_ts) / np.ptp(img_ns[f])
VIS._ProgressBar(12 + 4 * f / np.shape(img)[0],
20,
prefix='(%s):\t%8.3f sec' %
(code, time.time() - stpwch),
suffix='Temporal Smoothing...')
img_ = img_ns * np.ptp(img) / np.ptp(img_ns) # Renormalize #
img_ = img_[:, :, sz_off:-sz_off, :][:, :, :, sz_off:-sz_off] # Truncate #
## Threshold ##
VIS._ProgressBar(16,
20,
prefix='(%s):\t%8.3f sec' % (code, time.time() - stpwch),
suffix='Obtaining Threshold...')
eps_mu = _ApplyFilter(im.astype(np.float32), h_lt_)
eps_var = _ApplyFilter(im.astype(np.float32)**2, h_lt_) - eps_mu**2
# Compare statistics of the variance and the mean #
rat_min = np.minimum(np.min(eps_var) / np.min(eps_mu), 1)
rat_avg = np.minimum(np.mean(eps_var) / np.mean(eps_mu), 1)
rat_max = np.minimum(np.max(eps_var) / np.max(eps_mu), 1)
# Use to weight the mean #
eps_exp = np.sqrt(rat_min**2 + rat_avg**2 + rat_max**2)
eps_global = 0.0 #np.mean(img)**2/np.var(img)
eps_ = np.sqrt(
np.maximum(eps_mu**eps_exp - 1, 0) + 2 * eps_var + eps_global)
eps_ = eps_[:, :, sz_off:-sz_off, :][:, :, :, sz_off:-sz_off] # Truncate #
## Output ##
prefix = '(%s):\t%8.3f sec' % (code, time.time() - stpwch)
VIS._ProgressBar(20, 20, prefix=prefix, suffix='Finished! ')
return img_, ker_, eps_
def _Identify(pos, wgt, img, *, code, vis=False): ## Initialization ## F = len(wgt) clouds = list() # Progress # stpwch = time.time() timers = np.zeros((F)) tru = OP._LoadTruth(code) ## Cluster points in each frame ## for f in range(F): if(len(wgt[f]) == 0): continue # Check if there are any points in this frame # # Create a point cloud based on the points # pts = np.concatenate([pos[f], wgt[f][:,None]], axis=1) # Segment the point cloud to find clusters # cloud = PointCloud(pts, seg=True) # Why?? # vvv # # Weight threshold # clust = _CloudThr(cloud.clust, vis=vis) # # ^^^ # clust = _Separate(clust) # Append new clusters to the batch # clouds.extend(clust) ## Visualize ## """ if(vis): pts = np.zeros([0, 3]); for p in range(len(tru)): if(f in tru[p].frm): idx = np.nonzero(f == tru[p].frm)[0] pts = np.concatenate((pts, tru[p].res[idx,:]), axis=0) plt.figure() if(USER.KER_Z > 1): ax = plt.axes(position=[0,0,1,1], projection='3d') imclr = np.repeat((img/np.max(img))[f,0,:,:,None], 3, axis=2) xx, yy = np.meshgrid(range(np.shape(img)[2]), range(np.shape(img)[3])) ax.plot_surface(xx*USER.RES[0], yy*USER.RES[1], 0*xx, facecolors=imclr, rstride=8, cstride=8, zorder=-100000) for c in clust: ax.plot(c.abs[:,0], c.abs[:,1], c.abs[:,2], marker='o', linewidth=0, zorder=1000) else: ax = plt.axes(position=[0,0,1,1]) ax.imshow(img[f,0,:,:], cmap='gray') for c in clust: ax.scatter(c.pos[:,0], c.pos[:,1], s=400*c.wgt, c='b') ax.plot(pts[:,0], pts[:,1], color='r', marker='o', linewidth=0, fillstyle='none') plt.show() """ # Progress Display # timers[f] = time.time() - stpwch if(f > 0): prefix = '(%s):\t%8.3f sec' % (code, timers[f]) suffix = '(Remain: %5.0f sec)' % ((F-(f+1)) * np.mean(np.diff(timers[timers > 0]))) VIS._ProgressBar(f+1, F, prefix=prefix, suffix=suffix) ## Output ## return clouds
def _Stitch(traj, *, code=''): ## Initialize ## P = len(traj) links = np.zeros((P, P)) ## Determine Candidates ## for p in range(P-1): for q in range(p+1, P): # Frame distance # dist_f = np.abs(traj[q].tail.frm[0] - traj[p].head.frm[0]) if(USER.TRK_TOL < dist_f): continue # l2 distance # dist_l2 = np.sum((traj[p].head.m1a - traj[q].tail.m1a)**2) if(dist_f * USER.TRK_RAD**2 < dist_l2): continue # If they've passed, consider them # links[p,q] = np.sqrt(dist_l2) ## Evaluate Candidates ## pairs = np.array(np.nonzero(links)).T Q = len(np.unique(pairs[:,0])) stpwch = time.time() timers = np.zeros((Q)) for p in np.unique(pairs[:,0]): # Initialize # ord = 2 # Order of polynomial to fit # eps = list() # Maximum tolerance # err = [list(), list(), list()] # Error for each dimension # cand = pairs[pairs[:,0] == p, 1] # Find all candidates to this base particle # # Evaluate Candidates # for c in cand: # <idx in traj> # Get points close to the ends of each trajectory # rng_p = traj[p].head.frm[0] - traj[p].json[:,-2] <= USER.TRK_LEN rng_c = traj[c].json[:,-2] - traj[p].head.frm[0] <= USER.TRK_LEN + USER.TRK_TOL pts = np.concatenate((traj[p].json[rng_p,:], traj[c].json[rng_c,:]), axis=0) # Evaluate bounds # dom = np.power.outer(pts[:,-2], np.arange(ord+1)) eps_p = np.max(np.std(traj[p].json[rng_p,:3], axis=0)) eps_c = np.max(np.std(traj[c].json[rng_c,:3], axis=0)) eps.append(eps_p + eps_c) # Random Sample Consensus # beta = np.zeros((ord+1, 3)) for d in range(3): beta[:,d] = _RANSAC(pts[:,-2], pts[:,d], pts[:,-1], ord=ord) err[d].append(np.std(dom @ beta[:,d] - pts[:,d])) best = np.argmax(eps / np.max(err, axis=0)) # Merge base with best candidate # if(np.max(err, axis=0)[best] < eps[best]): # Initialze # cloud_base = traj[p].cloud cloud_cand = traj[cand[best]].cloud cloud_merge = list() frm_base = np.array([c.frm[0] for c in cloud_base]) frm_cand = np.array([c.frm[0] for c in cloud_cand]) frm_merge = np.union1d(frm_base, frm_cand) # Merge the base into the best candidate # for f in range(int(min(frm_merge)), int(max(frm_merge)+1)): if((f in frm_base) and (f in frm_cand)): # Compound both clouds # c_base = cloud_base[np.nonzero(f == frm_base)[0][0]] c_cand = cloud_cand[np.nonzero(f == frm_cand)[0][0]] c_total = PC(np.concatenate([c_base.pts, c_cand.pts], axis=0)) cloud_merge.append(c_total) elif((f in frm_base) and (f not in frm_cand)): # Take from base # cloud_merge.append(cloud_base[np.nonzero(f == frm_base)[0][0]]) elif((f not in frm_base) and (f in frm_cand)): # Take from cand # cloud_merge.append(cloud_cand[np.nonzero(f == frm_cand)[0][0]]) elif((f in rng_p) or (f in rng_c)): # Run from the model # X = np.power.outer(f, np.arange(ord+1)) pt = [0, 0, 0, f, np.mean([c.wgt for c in cloud_merge])] for d in range(3): pt[d] = X @ beta[:,d] cloud_merge.append(PC(pt)) # Discard the base and alter the candidate # # Note: by modifying only the past of the best candidate, we allow that candidate to be the base for another candidate particle traj[p].removed = True traj[cand[best]] = Particle(cloud_merge, hist=[traj[cand[best]].rho, traj[cand[best]].phi]) # Progress Bar # q = np.where(np.unique(pairs[:,0]) == p)[0][0] timers[q] = time.time() - stpwch if(sum(timers > 0) > 1): prefix = '(%s):\t%8.3f sec' % (code, timers[q]) suffix = '(Remain: %5.0f sec)' % ((Q-(q+1)) * np.mean(np.diff(timers[timers > 0]))) VIS._ProgressBar(1, 1, q, Q+1, prefix=prefix, suffix=suffix) ## Output ## return [traj[p] for p in range(P) if not traj[p].removed]
def _Track(clust, img, *, code='', step=1, vis=False): # Go frame by frame # frames = np.unique(np.concatenate([clust[c].frames for c in range(len(clust))])) F = len(frames) C = len(clust) stpwch = time.time() timers = np.zeros((F)) traj = list() for f in range(0, F, step): # Get all clusters belonging to this frame # clust_f = [clust[c] for c in range(C) if frames[f] in clust[c].frm] Cf = len(clust_f) # Go through each trajectory to see if any match # for p in range(len(traj)): if(not traj[p].active): continue # Identify clusters near the head of the trajectory # cand_acen = np.array([clust_f[c].m1a for c in range(Cf)]) sqdist = np.sum((traj[p].head.m1a - cand_acen)**2, axis=1) cand_idx = np.nonzero(sqdist < USER.TRK_RAD**2)[0] if(len(cand_idx) == 0): continue cand = [clust_f[c] for c in cand_idx] # Link! # traj_, best = traj[p].Link(cand) if(traj_ is not None): clust_f[cand_idx[best]].linked = True traj[p] = traj_ # Deactivate stagnant particles # for p in range(len(traj)): if(not traj[p].active): continue traj[p].active = (frames[f] - traj[p].head.frm[0] < USER.TRK_TOL) # Detect merge events # emptyclust = list() for p in range(len(traj)-1): if(not traj[p].active): continue for q in range(p+1, len(traj)): if(not traj[q].active): continue if(traj[p].head.frm[0] != traj[q].head.frm[0]): continue # Require same frame # # Check locality # if(np.sum((traj[p].head.ares - traj[q].head.ares)**2) < (2*USER.APR_RAD)**2): # They are basically the same particle at this point, but check if the recent histories align # rho_sim = np.allclose(traj[p].rho[:,:USER.TRK_MIN], traj[q].rho[:,:USER.TRK_MIN], atol=1E-3) if(not rho_sim): continue phi_sim = np.allclose(traj[p].phi[:,:USER.TRK_MIN,:], traj[q].phi[:,:USER.TRK_MIN,:], atol=1E-3) if(not phi_sim): continue # We have an overlap! Absorb the smaller one (q) # intersection = np.intersect1d(traj[p].frm, traj[q].frm) valid_frames = [f for f in traj[q].frm if f not in intersection] if(len(valid_frames) == 0): emptyclust.append(q); continue q_clouds = [traj[q].cloud[c] for c in range(traj[q].num) if traj[q].cloud[c].frm[0] in valid_frames] traj[q] = Particle(q_clouds) traj[q].active = False for e in np.sort(np.unique(emptyclust))[::-1]: traj.remove(traj[e]) # Particle cleanup # bad = [p for p in range(len(traj)) if(not traj[p].active and len(traj[p].cloud) < USER.TRK_MIN)] for b in bad[::-1]: traj.remove(traj[b]) # Create a new particle for each unlinked cluster # traj.extend([Particle([clust_f[c]]) for c in range(Cf) if not clust_f[c].linked]) # Progress Display # timers[f] = time.time() - stpwch if(f > 1): prefix = '(%s):\t%8.3f sec' % (code, timers[f]) suffix = '(Remain: %5.0f sec)' % ((F-(f+1)) * np.mean(np.diff(timers[timers > 0]))) VIS._ProgressBar(f+1, F, prefix=prefix, suffix=suffix) ## Output ## return traj
def TEST_LIMITS(img, ker, eps, *, code='', visual=False):
# Test runs for limitations only - 64x64! #
## Initialize ##
F = np.shape(img)[0]
Z = np.shape(img)[1]
Y = np.shape(img)[2]
X = np.shape(img)[3]
pos = [None] * F
wgt = [None] * F
H_A, H_S = _IDFilters([Z * np.shape(ker)[1], Y, X])
tru = OP._LoadTruth(code)
error = np.full([int(USER.REC_ITER // 3), F, 3], np.nan)
# Progress #
stpwch = time.time()
timers = np.zeros((F))
t_remain = np.nan
## Recover Emitter Positions ##
admm = ADMM(ker)
for f in np.arange(F):
psi_f = np.zeros((np.shape(ker)[0], Z * np.shape(ker)[1], Y, X))
for z in range(Z):
pb = (code, f, F, z, Z, 0, 1, 0, 1,
timers[f - (1 if (f > 0) else 0)], t_remain)
zrng = [z * USER.KER_Z, (z + 1) * USER.KER_Z]
# Split the image into each plane #
img_ = img[f, z, :, :] / eps[f, z, :, :]
eps_ = eps[f, z, :, :] / np.max(eps)
# Obtain the point clouds per frame #
psi_f[:, zrng[0]:zrng[1], ...], error = admm.Recover(img_,
code=code,
pb=pb,
vis=visual,
error=error)
# Identify points in the cloud #
Psi_f = npf.fftn(psi_f)
psi_a = np.real_if_close(
npf.ifftshift(npf.ifftn(Psi_f * H_A), axes=(-2, -1)))
psi_s = np.real_if_close(
npf.ifftshift(npf.ifftn(Psi_f * H_S), axes=(-2, -1)))
# Determine where the smaller blur is bigger than the larger one #
lhs = psi_a
rhs = psi_s * (1 + 1 / eps_) + np.mean(psi_f)
idx = np.nonzero(lhs > rhs)
pos[f] = np.array([idx[3], idx[2], idx[1], idx[0] / USER.KER_T + f]).T
wgt[f] = np.round(np.sqrt(psi_f**2 + ((psi_a + psi_s) / 2)**2)[idx], 3)
# Progress Display #
timers[f] = time.time() - stpwch
if (sum(timers > 0) > 1):
t_remain = (F - (f + 1)) * np.mean(np.diff(timers[timers > 0]))
prefix = '(%s):\t%8.3f sec' % (code, timers[f])
suffix = '(Remain: %5.0f sec)' % (t_remain)
VIS._ProgressBar(f + 1, F, prefix=prefix, suffix=suffix)
#if(error is not None):
spi.savemat(OP.FOLD_MAT + code + ' error.mat', {'error': error})
print(code + ' done!')
def Recover(self,
xi,
*,
code='',
pb=('', 0, 1, 0, 0, 0, 0, 0, 0, 0, 0),
vis=False,
error=None,
err=None):
## Initialization ##
# Hard-code the random seed to make it reproducible #
np.random.seed(0)
# Pass xi into the goal image `y`, but pass it through the selection matrix `sig` first #
y = np.zeros((np.shape(self.sig)[0], np.size(xi)))
y[0, ...] = xi.reshape(np.size(xi), order="F")
self.y[:] = np.reshape(self.sig @ y, self.sz, order="F")
self.y_ = self.y.copy()
# Reinitialize psi and the slack variables #
self.Psi[:] = fftw.zeros_aligned(self.sz, dtype='complex64')
self.S_0[:] = fftw.zeros_aligned(self.sz, dtype='complex64')
self.S_1[:] = fftw.zeros_aligned(self.sz, dtype='complex64')
self.S_2[:] = fftw.zeros_aligned(self.sz, dtype='complex64')
# Localization error #
if (vis or (error is not None)):
pts = np.zeros([0, 3])
if (error is not None):
tru = OP._LoadMot(code)
pts = tru[tru[:, :, 3] == pb[1], :]
temp = []
temp[:] = pts[:, 0]
pts[:, 0] = pts[:, 1] # Swap X & Y #
pts[:, 1] = temp[:]
H_A, H_S = _IDFilters(self.sz[1:])
else:
tru = OP._LoadTruth(code)
for p in range(len(tru)):
if (pb[1] in tru[p].frm):
f = np.nonzero(pb[1] == tru[p].frm)[0]
pts = np.concatenate((pts, tru[p].res[f, :]), axis=0)
## Iterate Through ADMM ##
stpwch = time.time()
timers = np.zeros((USER.REC_ITER))
for i in range(USER.REC_ITER):
# Separate the result of this iteration from the previous solution `self.Psi`. This allows us to build Psi incrementally, modularizing the regularization.
Psi = fftw.zeros_aligned(self.sz, dtype='complex64')
# Perform Regularizations #
Psi = self.Reg_Accuracy(Psi, i)
Psi = self.Reg_Sparcity(
Psi, 1) #np.minimum(np.maximum(2*i/USER.REC_ITER, 1/2), 3/2))
Psi = self.Reg_Temporal(Psi)
# Copy in the new result #
self.Psi[:] = Psi.copy()
# Alert us if we have an issue! #
if (np.any(np.isnan(Psi))): raise ValueError("Psi has NaN values!")
# Visualization #
if (vis and (np.mod(i, USER.REC_ITER // 20) == 0)):
self.BT_Psi()
plt.clf()
plt.gca(position=[0, 0, 1, 1])
plt.imshow(np.log10(
npf.fftshift(np.sum(np.abs(self.psi), axis=(0, 1)))),
cmap='gray')
plt.plot(pts[:, 0],
pts[:, 1],
color='r',
marker='o',
linewidth=0,
fillstyle='none')
plt.clim(-3, 0)
plt.draw()
plt.pause(0.1)
if ((error is not None) and (np.mod(i, 3) == 0) and (i > 60)):
# Get psi #
self.BT_Psi()
# Find where psi is important #
psi_f = npf.fftshift(np.abs(self.psi), axes=(-2, -1))
# Identify points in the cloud #
Psi_f = npf.fftn(psi_f)
psi_a = np.real_if_close(
npf.ifftshift(npf.ifftn(Psi_f * H_A), axes=(-2, -1)))
psi_s = np.real_if_close(
npf.ifftshift(npf.ifftn(Psi_f * H_S), axes=(-2, -1)))
lhs = psi_a
rhs = psi_s * (1 + 1) + np.mean(psi_f)
idx = np.nonzero(lhs > rhs)
pos = np.array([idx[3], idx[2], idx[1], idx[0] / USER.KER_T]).T
wgt = np.round(
np.sqrt(psi_f**2 + ((psi_a + psi_s) / 2)**2)[idx], 3)
if (0 < len(wgt) < 10000):
# Attempt a triangulation #
# Create a point cloud based on the points #
pnts = np.concatenate([pos, wgt[:, None]], axis=1)
# Segment the point cloud to find clusters #
cloud = PointCloud(pnts, seg=True)
# Why?? # vvv #
# Weight threshold #
clust = _CloudThr(cloud.clust)
# # ^^^ #
clust = _Separate(clust)
if (len(clust) == 0): continue
# Evaluate the average minimum error per particle #
dist_x = np.zeros([np.shape(pts)[0], len(clust)])
dist_y = np.zeros([np.shape(pts)[0], len(clust)])
dist_z = np.zeros([np.shape(pts)[0], len(clust)])
# Evaluate the distance between each point and all clusters #
for c in range(len(clust)):
diff = (pts[:, :3] - clust[c].res) * [
*USER.RES, USER.DOF[0] / USER.KER_Z
]
dist_x[:, c] = np.abs(diff[:, 0])
dist_y[:, c] = np.abs(diff[:, 1])
dist_z[:, c] = np.abs(diff[:, 2])
# Get the minimum error per cluster, average over all particles #
error[int(i // 3), pb[1], 0] = np.mean(np.min(dist_x,
1)) # X
error[int(i // 3), pb[1], 1] = np.mean(np.min(dist_y,
1)) # Y
error[int(i // 3), pb[1], 2] = np.mean(np.min(dist_z,
1)) # Z
#if((err is not None) and (np.mod(i, 3) == 0)):
# err[pb(1),i,:] = ComputeError(xi)
# Progress Bar #
timers[i] = time.time() - stpwch
if (i > 0):
prefix = '(%s):\t%8.3f sec' % (pb[0], pb[-2] + timers[i])
#suffix = '(Remain: %5.0f sec)' % (pb[-1])
suffix = '(Remain: %3.0f:%2.0f:%2.0f) ' % (pb[-1] // 3600,
(pb[-1] % 3600) //
60, pb[-1] % 60)
if (pb[4] > 1): # Show Z progress #
VIS._ProgressBar(pb[1] + 1,
pb[2],
sub_i=pb[3] + 1,
sub_I=pb[4],
prefix=prefix,
suffix=suffix)
elif (pb[6] > 1
or pb[8] > 1): # Show chunked iteration progress #
i_ = i + 1 + (pb[5] * pb[8] + pb[7]) * USER.REC_ITER
I_ = pb[6] * pb[8] * USER.REC_ITER
VIS._ProgressBar(pb[1] + 1,
pb[2],
sub_i=i_,
sub_I=I_,
prefix=prefix,
suffix=suffix)
else:
VIS._ProgressBar(pb[1] + 1,
pb[2],
sub_i=i,
sub_I=USER.REC_ITER,
prefix=prefix,
suffix=suffix)
if (vis):
plt.ioff()
plt.show()
## Output ##
self.BT_Psi()
return np.abs(self.psi), error
def _Recover(img, ker, eps, *, code='', step=1, vis=False):
## Initialize ##
f0 = 0
F = np.shape(img)[0]
Z = np.shape(img)[1]
Y = np.shape(img)[2]
X = np.shape(img)[3]
C = np.minimum(X, Y) if ((not USER.REC_CHUNK) or ((X <= 128) and
(Y <= 128))) else 64
pos = [None] * F
wgt = [None] * F
H_A, H_S = _IDFilters([np.shape(ker)[0], Z * np.shape(ker)[1], Y, X])
tru = OP._LoadTruth(code)
# Progress #
stpwch = time.time()
timers = np.zeros((F))
t_remain = np.nan
# Truth #
#error = np.full([int(USER.REC_ITER//3), F], np.nan)
## Recover Emitter Positions ##
ker_ = ker[..., (Y - C) // 2:(Y + C) // 2, :][...,
(X - C) // 2:(X + C) // 2]
admm = ADMM(ker_)
for f in np.arange(F, step=step):
psi_f = np.zeros((np.shape(ker)[0], Z * np.shape(ker)[1], Y, X))
for z in range(Z):
zrng = [z * USER.KER_Z, (z + 1) * USER.KER_Z]
# Split the image into each plane #
img_ = img[f + f0, z, :, :] / eps[
f + f0,
z, :, :] # << ------------------------------------------------------------ #
eps_ = eps[f + f0, z, :, :] / np.max(eps)
# Chunk the image and obtain point clouds per frame #
img_chunks, xrng, yrng, overlay = _Chunk(img_, C=C)
M = np.shape(xrng)[0]
N = np.shape(yrng)[0]
for m in range(M):
for n in range(N):
pb = (code, f, F, z, Z, m, M, n, N,
timers[f -
(1 if (M == 1 and N == 1 and f > 0) else 0)],
t_remain)
if (np.ptp(img_chunks[n, m, ...]) > 2 * np.std(img_)):
psi, _ = admm.Recover(img_chunks[n, m, ...],
code=code,
pb=pb,
vis=False)
psi = np.fft.fftshift(psi, axes=(-2, -1))
psi_f[:, zrng[0]:zrng[1],
...][...,
yrng[n,
0]:yrng[n,
1], :][...,
xrng[m,
0]:xrng[m,
1]] += psi
timers[f] = time.time() - stpwch
psi_f[:, zrng[0]:zrng[1], ...] /= np.maximum(overlay, 1)
# Identify points in the cloud #
Psi_f = npf.fftn(psi_f)
psi_a = np.real_if_close(
npf.ifftshift(npf.ifftn(Psi_f * H_A), axes=(-2, -1)))
psi_s = np.real_if_close(
npf.ifftshift(npf.ifftn(Psi_f * H_S), axes=(-2, -1)))
# Determine where the smaller blur is bigger than the larger one #
lhs = psi_a
rhs = psi_s * (1 + 1 / eps_) + np.mean(psi_f) * (
1 + (USER.KER_T > 1)) #eps_ * np.std(psi_f)/np.mean(psi_f)
idx = np.nonzero(lhs > rhs)
pos[f] = np.array([idx[3], idx[2], idx[1], idx[0] / USER.KER_T + f]).T
wgt[f] = np.round(np.sqrt(psi_f**2 + ((psi_a + psi_s) / 2)**2)[idx], 3)
# Visualization #
if (vis):
plt.figure(figsize=(15, 5))
ax = plt.axes(position=[0, 0, 1 / 3, 0.9])
ax.imshow(img_, cmap='gray')
ax.set_title('Input image #%i/%i' % (f + 1, F + 1))
ax = plt.axes(position=[1 / 3, 0, 1 / 3, 0.9])
ax.imshow(np.sum(psi_f, axis=(0, 1)), cmap='gray')
ax.set_title('Deconvolution')
ax = plt.axes(position=[2 / 3, 0, 1 / 3, 0.9])
ax.imshow(img_, cmap='gray')
if (len(wgt[f]) > 0):
ax.scatter(pos[f][:, 0],
pos[f][:, 1],
s=100 * (wgt[f] / np.max(wgt[f])),
c='r')
ax.set_title('Point Cloud')
if (USER.KER_Z > 1):
plt.figure(figsize=(6, 6))
ax = plt.axes(projection='3d',
position=[-0.05, -0.07, 1.1, 1.1])
if (len(wgt[f]) > 0):
ax.scatter(pos[f][:, 0],
pos[f][:, 1],
pos[f][:, 2],
s=100 * (wgt[f] / np.max(wgt[f])))
ax.view_init(azim=30, elev=10)
ax.set_xlim(0, np.shape(img)[3])
ax.set_ylim(0, np.shape(img)[2])
ax.set_zlim(0, USER.KER_Z)
plt.show()
# Progress Display #
timers[f] = time.time() - stpwch
if (sum(timers > 0) > 1):
t_remain = (F - (f + 1)) * np.mean(np.diff(timers[timers > 0]))
prefix = '(%s):\t%8.3f sec' % (code, timers[f])
#suffix = '(Remain: %5.0f sec)' % (t_remain)
suffix = '(Remain: %3.0f:%2.0f:%2.0f)' % (t_remain // 3600,
(t_remain % 3600) // 60,
t_remain % 60)
VIS._ProgressBar(f + 1, F, prefix=prefix, suffix=suffix)
#import scipy.io as spi
#spi.savemat(OP.FOLD_MAT + code + ' error.mat', {'error': error})
## Output ##
return pos, wgt