def tile_and_correct_wrapper(params):
from skimage.external.tifffile import imread
import numpy as np
import cv2
try:
cv2.setNumThreads(1)
except:
1 # 'Open CV is naturally single threaded'
from caiman.motion_correction import tile_and_correct
img_name, out_fname, idxs, shape_mov, template, strides, overlaps, max_shifts,\
add_to_movie, max_deviation_rigid, upsample_factor_grid, newoverlaps, newstrides = params
imgs = imread(img_name, key=idxs)
mc = np.zeros(imgs.shape, dtype=np.float32)
shift_info = []
for count, img in enumerate(imgs):
if count % 10 == 0:
print(count)
mc[count], total_shift, start_step, xy_grid = tile_and_correct(img, template, strides, overlaps, max_shifts, add_to_movie=add_to_movie, newoverlaps=newoverlaps, newstrides=newstrides,
upsample_factor_grid=upsample_factor_grid, upsample_factor_fft=10, show_movie=False, max_deviation_rigid=max_deviation_rigid)
shift_info.append([total_shift, start_step, xy_grid])
if out_fname is not None:
outv = np.memmap(out_fname, mode='r+', dtype=np.float32,
shape=shape_mov, order='F')
outv[:, idxs] = np.reshape(
mc.astype(np.float32), (len(imgs), -1), order='F').T
return shift_info, idxs, np.nanmean(mc, 0)
def _processFrame(self, frame, frame_number):
''' Do some basic processing on a single frame
Raises NaNFrameException if a frame contains NaN
Returns the normalized/etc modified frame
'''
t = time.time()
if frame is None:
raise ObjectNotFoundError
if np.isnan(np.sum(frame)):
raise NaNFrameException
frame = frame.astype(
np.float32) #or require float32 from image acquistion
if self.onAc.params.get('online', 'ds_factor') > 1:
frame = cv2.resize(frame, self.onAc.img_norm.shape[::-1])
# TODO check for params, onAc componenets before calling, or except
if self.onAc.params.get('online', 'normalize'):
frame -= self.onAc.img_min
if self.onAc.params.get('online', 'motion_correct'):
try:
templ = self.onAc.estimates.Ab.dot(
self.onAc.estimates.C_on[:self.onAc.M, (frame_number - 1)]
).reshape(
# self.onAc.estimates.C_on[:self.onAc.M, (frame_number-1)%self.onAc.window]).reshape(
self.onAc.params.get('data', 'dims'),
order='F') * self.onAc.img_norm
except Exception as e:
logger.error('Unknown exception {0}'.format(e))
raise Exception
if self.onAc.params.get('motion', 'pw_rigid'):
frame_cor, shift = tile_and_correct(
frame,
templ,
self.onAc.params.motion['strides'],
self.onAc.params.motion['overlaps'],
self.onAc.params.motion['max_shifts'],
newoverlaps=None,
newstrides=None,
upsample_factor_grid=4,
upsample_factor_fft=10,
show_movie=False,
max_deviation_rigid=self.onAc.params.
motion['max_deviation_rigid'],
add_to_movie=0,
shifts_opencv=True,
gSig_filt=None,
use_cuda=False,
border_nan='copy')[:2]
else:
frame_cor, shift = motion_correct_iteration_fast(
frame, templ, self.max_shifts_online,
self.max_shifts_online)
self.onAc.estimates.shifts.append(shift)
else:
frame_cor = frame
if self.onAc.params.get('online', 'normalize'):
frame_cor = frame_cor / self.onAc.img_norm
self.procFrame_time.append([time.time() - t])
return frame_cor
#newshapes = (32,32)
newstrides = None
##Sue Ann
#overlaps = (32,32)
#strides = (128,128)
#%
total_shifts = []
start_steps = []
xy_grids = []
mc = np.zeros(m.shape)
for count, img in enumerate(np.array(m)):
if count % 10 == 0:
print(count)
mc[count],total_shift,start_step,xy_grid = tile_and_correct(img, template, strides, overlaps,(12,12), newoverlaps = None, \
newstrides = newstrides, upsample_factor_grid=4,\
upsample_factor_fft=10,show_movie=False,max_deviation_rigid=2,add_to_movie=add_to_movie)
total_shifts.append(total_shift)
start_steps.append(start_step)
xy_grids.append(xy_grid)
#%%
pl.plot(np.reshape(np.array(total_shifts), (len(total_shifts), -1)))
#%%
m_raw = np.nanmean(m, 0)
m_rig = np.nanmean(mr, 0)
m_el = np.nanmean(mc, 0)
#%%
import scipy
r_raw = []
def __fit_next_frame(self, frame, t, model_LN=None, out=None):
ssub_B = self.params.get('init', 'ssub_B') * self.params.get(
'init', 'ssub')
d1, d2 = self.params.get('data', 'dims')
max_shifts_online = self.params.get('online', 'max_shifts_online')
if model_LN is not None:
if self.params.get('ring_CNN', 'remove_activity'):
activity = self.estimates.Ab[:, :self.N].dot(
self.estimates.C_on[:self.N,
t - 1]).reshape(self.params.get(
'data', 'dims'),
order='F')
if self.params.get('online', 'normalize'):
activity *= self.img_norm
else:
activity = 0.
# frame = frame.astype(np.float32) - activity
frame = frame - np.squeeze(
model_LN.predict(
np.expand_dims(
np.expand_dims(frame.astype(np.float32) - activity, 0),
-1)))
frame = np.maximum(frame, 0)
t_frame_start = time.time()
if np.isnan(np.sum(frame)):
raise Exception('Current frame contains NaN')
frame_ = frame.copy().astype(np.float32)
if self.params.get('online', 'ds_factor') > 1:
frame_ = cv2.resize(frame_, self.img_norm.shape[::-1])
if self.params.get('online', 'normalize'):
frame_ -= self.img_min # make data non-negative
if self.params.get('online', 'motion_correct'):
templ = self.estimates.Ab.dot(
np.median(self.estimates.C_on[:self.M, t - 51:t - 1],
1)).reshape(self.params.get('data', 'dims'),
order='F') #*self.img_norm
if self.is1p and self.estimates.W is not None:
if ssub_B == 1:
B = self.estimates.W.dot((frame_ - templ).flatten(
order='F') - self.estimates.b0) + self.estimates.b0
B = B.reshape(self.params.get('data', 'dims'), order='F')
else:
b0 = self.estimates.b0.reshape((d1, d2),
order='F') #*self.img_norm
bc2 = initialization.downscale(
frame_ - templ - b0,
(ssub_B, ssub_B)).flatten(order='F')
Wb = self.estimates.W.dot(bc2).reshape(
((d1 - 1) // ssub_B + 1, (d2 - 1) // ssub_B + 1),
order='F')
B = b0 + np.repeat(np.repeat(Wb, ssub_B, 0), ssub_B,
1)[:d1, :d2]
templ += B
if self.params.get('online', 'normalize'):
templ *= self.img_norm
if self.is1p:
templ = high_pass_filter_space(templ,
self.params.motion['gSig_filt'])
if self.params.get('motion', 'pw_rigid'):
frame_cor, shift, _, xy_grid = tile_and_correct(
frame_,
templ,
self.params.motion['strides'],
self.params.motion['overlaps'],
self.params.motion['max_shifts'],
newoverlaps=None,
newstrides=None,
upsample_factor_grid=4,
upsample_factor_fft=10,
show_movie=False,
max_deviation_rigid=self.params.
motion['max_deviation_rigid'],
add_to_movie=0,
shifts_opencv=True,
gSig_filt=None,
use_cuda=False,
border_nan='copy')
else:
if self.is1p:
frame_orig = frame_.copy()
frame_ = high_pass_filter_space(
frame_, self.params.motion['gSig_filt'])
frame_cor, shift = motion_correct_iteration_fast(
frame_, templ, max_shifts_online, max_shifts_online)
if self.is1p:
M = np.float32([[1, 0, shift[1]], [0, 1, shift[0]]])
frame_cor = cv2.warpAffine(frame_orig,
M,
frame_.shape[::-1],
flags=cv2.INTER_CUBIC,
borderMode=cv2.BORDER_REFLECT)
self.estimates.shifts.append(shift)
else:
templ = None
frame_cor = frame_
self.current_frame_cor = frame_cor
if self.params.get('online', 'normalize'):
frame_cor = frame_cor / self.img_norm
self.fit_next(t, frame_cor.reshape(-1, order='F'))
if self.fp_detector.method is not None:
self.__reject_fp_comps((d1, d2), max_bright=frame_.max())
#newshapes = (32,32)
newstrides = None
##Sue Ann
#overlaps = (32,32)
#strides = (128,128)
#%
total_shifts = []
start_steps = []
xy_grids = []
mc = np.zeros(m.shape)
for count,img in enumerate(np.array(m)):
if count % 10 == 0:
print(count)
mc[count],total_shift,start_step,xy_grid = tile_and_correct(img, template, strides, overlaps,(12,12), newoverlaps = None, \
newstrides = newstrides, upsample_factor_grid=4,\
upsample_factor_fft=10,show_movie=False,max_deviation_rigid=2,add_to_movie=add_to_movie)
total_shifts.append(total_shift)
start_steps.append(start_step)
xy_grids.append(xy_grid)
#%%
pl.plot(np.reshape(np.array(total_shifts),(len(total_shifts),-1)))
#%%
m_raw = np.nanmean(m,0)
m_rig = np.nanmean(mr,0)
m_el = np.nanmean(mc,0)
#%%
import scipy
r_raw = []
