#%%
if False:
m=cb.load(filename_py);
#%%
m=cb.load(filename_hdf5);
#m=m.crop(crop_top=0,crop_bottom=1,crop_left=0,crop_right=0,crop_begin=0,crop_end=0);
min_val_add=np.min(np.mean(m,axis=0))
#min_val_add=np.min(m)-1# movies must be strictly positive!!
m=m-min_val_add
#%% concatenate movies (it will add to the original movie)
# you have to create another movie new_mov=XMovie(...)
if False:
conc_mov=cb.concatenate([m,m])
#%% quick and dirt
m=cb.load(filename_hdf5);
m,shifts,xcorrs,template=m.motion_correct(max_shift_w=5,max_shift_h=5, num_frames_template=None, template = None,method='opencv')
#%% motion correct for template purpose. Just use a subset to compute the template
if type_ == 'granule':
submov=m[::1,:]
max_shift_h=5;
max_shift_w=5;
else:
filename_hdf5 = filename[:-4]+'.hdf5'
filename_mc = filename[:-4]+'_mc.npz'
frameRate = 30
start_time = 0
# load movie
# for loading only a portion of the movie or only some channels
# you can use the option: subindices=range(0,1500,10)
m = cb.load(filename, fr=frameRate, start_time=start_time)
# red and green channels
m_r=m[:,:,:,0]
m=m[:,:,:,1]
# backend='opencv' is much faster
cb.concatenate([m,m_r],axis=1).resize(.5,.5,.1).play(fr=100, gain=1.0, magnification=1,backend='opencv')
# automatic parameters motion correction
max_shift_h = 20 # maximum allowed shifts in y
max_shift_w = 20 # maximum allowed shifts in x
m_r_mc, shifts, xcorrs, template = m_r.motion_correct(max_shift_w=max_shift_w,
max_shift_h=max_shift_h,
num_frames_template=None,
template=None, remove_blanks=False,
method='opencv')
#%%
pl.figure()
pl.imshow(template,cmap='gray')
pl.figure()
pl.plot(shifts)
num_frames_median=1000;
movs=[];
fr=30;
start_time=0;
templates=[];
for tif_file in tif_files:
print(tif_file)
m=cb.load(tif_file,fr=30,start_time=0,subindices=range(0,1500,20))
min_val_add=np.percentile(m,.01)
m=m-min_val_add
movs.append(m)
templ=np.nanmedian(m,axis=0);
m,template,shifts,xcorrs=m.motion_correct(max_shift_w=5, max_shift_h=5, show_movie=False, template=templ, method='opencv')
templates.append(np.median(m,axis=0))
all_movs=cb.concatenate(movs)
m=cb.movie(np.array(templates),fr=1)
m=m.motion_correct(template=m[0])[0]
template=np.median(m,axis=0)
cb.matrixMontage(m,cmap=pl.cm.gray,vmin=0,vmax=1000)
#%%
all_shifts=[];
movs=[];
for tif_file in tif_files:
print(tif_file)
m=cb.load(tif_file,fr=30,start_time=0);
min_val_add=np.percentile(m,.01)
m=m-min_val_add
m,_,shifts,_=m.motion_correct(template=template, method='opencv')
movs.append(m)
# save movie hdf5 format. fastest
m.save(filename_hdf5)
# backend='opencv' is much faster
m.play(fr=100, gain=15.0, magnification=1, backend='opencv')
low_SNR = True
if low_SNR:
N = 1000000
mn1 = m.copy().bilateral_blur_2D(diameter=5,
sigmaColor=10000,
sigmaSpace=0)
mn1, shifts, xcorrs, template = mn1.motion_correct()
mn2 = mn1.apply_shifts(shifts)
# mn1=cb.movie(np.transpose(np.array(Y_n),[2,0,1]),fr=30)
mn = cb.concatenate([mn1, mn2], axis=1)
# automatic parameters motion correction
max_shift_h = 10 # maximum allowed shifts in y
max_shift_w = 10 # maximum allowed shifts in x
m = cb.load(filename_hdf5)
m, shifts, xcorrs, template = m.motion_correct(max_shift_w=max_shift_w,
max_shift_h=max_shift_h,
num_frames_template=None,
template=None,
method='opencv')
# removie the borders
max_h, max_w = np.max(shifts, axis=0)
min_h, min_w = np.min(shifts, axis=0)
m = m.crop(crop_top=max_h,
X1 = mdl1.components_.T
pl.figure()
for idx, mm in enumerate(X1.T):
pl.subplot(7, 7, idx + 1)
pl.imshow(np.reshape(mm, (d1, d2), order='F'), cmap=pl.cm.gray,
vmin=np.percentile(mm, 1), vmax=np.percentile(mm, 99))
pl.subplot(7, 7, idx + 2)
mu = np.mean(m1, 0)
pl.imshow(mu, cmap=pl.cm.gray, vmin=np.percentile(
mu, 1), vmax=np.percentile(mu, 99))
#%% PLAY DENOISED MOVIE
denoised = cb.movie(np.reshape(traces.dot(
X1.T), [T, d1, d2], order='F'), fr=30)
denoised = cb.concatenate([denoised, cb.movie(np.transpose(
np.array(Y[:, :, N:N1]), [2, 0, 1]), fr=30)], axis=2)
# denoised=cb.concatenate([denoised,m1],axis=1)
(denoised - np.mean(denoised, 0)).play(gain=8.,
magnification=8, backend='opencv', fr=30)
#%% PLAY DENOISED MOVIE ONLY UYSING SOME COMPONENTS
idx = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
idx = np.array([0, 12])
denoised = cb.movie(np.reshape(traces[:, idx].dot(
X1[:, idx].T), [T, d1, d2], order='F'), fr=30)
(denoised - np.mean(denoised, 0).min()).play(gain=5.,
magnification=8, backend='opencv', fr=30)
#%% PLAY RESIDUAL MOVIE
residual = m1 - denoised
filename_mc = filename[:-4] + '_mc.npz'
frameRate = 30
start_time = 0
# load movie
# for loading only a portion of the movie or only some channels
# you can use the option: subindices=range(0,1500,10)
m = cb.load(filename, fr=frameRate, start_time=start_time)
# red and green channels
m_r = m[:, :, :, 0]
m = m[:, :, :, 1]
# backend='opencv' is much faster
cb.concatenate([m, m_r], axis=1).resize(.5, .5, .1).play(fr=100,
gain=1.0,
magnification=1,
backend='opencv')
# automatic parameters motion correction
max_shift_h = 20 # maximum allowed shifts in y
max_shift_w = 20 # maximum allowed shifts in x
m_r_mc, shifts, xcorrs, template = m_r.motion_correct(max_shift_w=max_shift_w,
max_shift_h=max_shift_h,
num_frames_template=None,
template=None,
remove_blanks=False,
method='opencv')
#%%
pl.figure()
pl.imshow(template, cmap='gray')
pl.figure()
# save movie hdf5 format. fastest
m.save(filename_hdf5)
# backend='opencv' is much faster
m.play(fr=100, gain=15.0, magnification=1, backend='opencv')
low_SNR = True
if low_SNR:
N = 1000000
mn1 = m.copy().bilateral_blur_2D(diameter=5,
sigmaColor=10000,
sigmaSpace=0)
mn1, shifts, xcorrs, template = mn1.motion_correct()
mn2 = mn1.apply_shifts(shifts)
# mn1=cb.movie(np.transpose(np.array(Y_n),[2,0,1]),fr=30)
mn = cb.concatenate([mn1, mn2], axis=1)
# automatic parameters motion correction
max_shift_h = 10 # maximum allowed shifts in y
max_shift_w = 10 # maximum allowed shifts in x
m = cb.load(filename_hdf5)
m, shifts, xcorrs, template = m.motion_correct(max_shift_w=max_shift_w,
max_shift_h=max_shift_h,
num_frames_template=None,
template=None,
method='opencv')
# removie the borders
max_h, max_w = np.max(shifts, axis=0)
min_h, min_w = np.min(shifts, axis=0)
m = m.crop(crop_top=max_h,
