def HighPassFilter(t, px, py, moco_kernel, central_fix):
'''
Slow drift correction by robust high-pass filtering
Effectively forces long-term average pupil fixation to be centrally
fixated in video space.
Arguments
----
t : 1D float array
Video soft timestamps in seconds
px : 1D float array
Video space pupil center x
py : 1D float array
Video space pupil center y
moco_kernel : integer
Temporal kernel width in samples [31]
central_fix : float tuple
(x,y) coordinate in video space of central fixation
Returns
----
px_filt : 1D float array
Drift corrected video space pupil center x
py_filt : 1D float array
Drift corrected video space pupil center y
'''
# Force odd-valued kernel width
moco_kernel = utils._forceodd(moco_kernel)
print(' Highpass filtering with %d sample kernel' % moco_kernel)
# Infill NaN regions
# Replace NaNs with unreasonable but finite value for median filtering
nan_idx = np.isnan(px)
px[nan_idx] = -1e9
py[nan_idx] = -1e9
# Moving median filter to estimate baseline
px_bline = medfilt(px, moco_kernel)
py_bline = medfilt(py, moco_kernel)
# Restore NaNs to vectors
px_bline[nan_idx] = np.nan
py_bline[nan_idx] = np.nan
# Subtract baseline and add central fixation offset
px_filt = px - px_bline + central_fix[0]
py_filt = py - py_bline + central_fix[1]
return px_filt, py_filt, px_bline, py_bline
def HighPassFilter(t, px, py, moco_kernel, central_fix):
"""
Slow drift correction by robust high-pass filtering
Effectively forces long-term average pupil fixation to be centrally
fixated in video space.
Arguments
----
t : 1D float array
Video soft timestamps in seconds
px : 1D float array
Video space pupil center x
py : 1D float array
Video space pupil center y
moco_kernel : integer
Temporal kernel width in samples [31]
central_fix : float tuple
(x,y) coordinate in video space of central fixation
Returns
----
px_filt : 1D float array
Drift corrected video space pupil center x
py_filt : 1D float array
Drift corrected video space pupil center y
"""
# Force odd-valued kernel width
moco_kernel = utils._forceodd(moco_kernel)
print(" Highpass filtering with %d sample kernel" % moco_kernel)
# Infill NaN regions
# Replace NaNs with unreasonable but finite value for median filtering
nan_idx = np.isnan(px)
px[nan_idx] = -1e9
py[nan_idx] = -1e9
# Moving median filter to estimate baseline
px_bline = medfilt(px, moco_kernel)
py_bline = medfilt(py, moco_kernel)
# Restore NaNs to vectors
px_bline[nan_idx] = np.nan
py_bline[nan_idx] = np.nan
# Subtract baseline and add central fixation offset
px_filt = px - px_bline + central_fix[0]
py_filt = py - py_bline + central_fix[1]
return px_filt, py_filt, px_bline, py_bline
def FilterPupilometry(pupils_csv, pupils_filt_csv):
'''
DEPRECATED: Temporally filter all pupilometry timeseries
'''
if not os.path.isfile(pupils_csv):
print('* Raw pupilometry CSV file missing - returning')
return False
# Read raw pupilometry data
p = ReadPupilometry(pupils_csv)
# Sampling time (s)
dt = p[1,0] - p[0,0]
# Kernel widths for each metric
k_area = utils._forceodd(0.25 / dt)
k_pupil = 3
k_blink = utils._forceodd(0.25 / dt)
k_art = utils._forceodd(1.0 / dt)
# Moving median filter
pf = p.copy()
pf[:,1] = utils._nanmedfilt(p[:,1], k_area)
pf[:,2] = utils._nanmedfilt(p[:,2], k_pupil) # Pupil x
pf[:,3] = utils._nanmedfilt(p[:,3], k_pupil) # Pupil y
# Blink filter
pf[:,4] = utils._nanmedfilt(p[:,8], k_blink)
# Artifact power
pf[:,5] = utils._nanmedfilt(pf[:,9], k_art)
# Write filtered timeseries to new CSV file in results directory
np.savetxt(pupils_filt_csv, pf, fmt='%.6f', delimiter=',')
# Clean return
return True
def FilterPupilometry(pupils_csv, pupils_filt_csv):
'''
DEPRECATED: Temporally filter all pupilometry timeseries
'''
if not os.path.isfile(pupils_csv):
print('* Raw pupilometry CSV file missing - returning')
return False
# Read raw pupilometry data
p = ReadPupilometry(pupils_csv)
# Sampling time (s)
dt = p[1, 0] - p[0, 0]
# Kernel widths for each metric
k_area = utils._forceodd(0.25 / dt)
k_pupil = 3
k_blink = utils._forceodd(0.25 / dt)
k_art = utils._forceodd(1.0 / dt)
# Moving median filter
pf = p.copy()
pf[:, 1] = utils._nanmedfilt(p[:, 1], k_area)
pf[:, 2] = utils._nanmedfilt(p[:, 2], k_pupil) # Pupil x
pf[:, 3] = utils._nanmedfilt(p[:, 3], k_pupil) # Pupil y
# Blink filter
pf[:, 4] = utils._nanmedfilt(p[:, 8], k_blink)
# Artifact power
pf[:, 5] = utils._nanmedfilt(pf[:, 9], k_art)
# Write filtered timeseries to new CSV file in results directory
np.savetxt(pupils_filt_csv, pf, fmt='%.6f', delimiter=',')
# Clean return
return True
def EstimateBias(fr):
'''
Estimate illumination bias field
Arguments
----
fr : 2D numpy uint8 array
Uncorrected image with biased illumination
Returns
----
bias_field : 2D numpy float array
Estimated bias multiplier field
'''
# Target downsampled matrix size
nd = 32;
# Get image dimensions
ny, nx, nz = fr.shape
# Target maximum dimension is 32
# Apect ratio preserved approximately
if nx > ny:
nxd = nd
nyd = int(nx/32.0 * ny)
else:
nxd = int(ny/32.0 * nx)
nyd = nd
# Downsample frame
fr_d = cv2.resize(fr, (nxd, nyd))
# 2D baseline estimation
# Use large kernel relative to image size
k = utils._forceodd(nd/2)
bias_field_d = cv2.medianBlur(fr_d, k)
# Bias correction
bias_corr_d = 1 - (bias_field_d - np.mean(fr_d)) / fr_d
# Upsample biasfield to same size as original frame
bias_corr = cv2.resize(bias_corr_d, (nx, ny))
# DEBUG: Flat bias correction
bias_corr = np.ones_like(fr)
return bias_corr
def EstimateBias(fr):
'''
Estimate illumination bias field
Arguments
----
fr : 2D numpy uint8 array
Uncorrected image with biased illumination
Returns
----
bias_field : 2D numpy float array
Estimated bias multiplier field
'''
# Target downsampled matrix size
nd = 32
# Get image dimensions
ny, nx, nz = fr.shape
# Target maximum dimension is 32
# Apect ratio preserved approximately
if nx > ny:
nxd = nd
nyd = int(nx / 32.0 * ny)
else:
nxd = int(ny / 32.0 * nx)
nyd = nd
# Downsample frame
fr_d = cv2.resize(fr, (nxd, nyd))
# 2D baseline estimation
# Use large kernel relative to image size
k = utils._forceodd(nd / 2)
bias_field_d = cv2.medianBlur(fr_d, k)
# Bias correction
bias_corr_d = 1 - (bias_field_d - np.mean(fr_d)) / fr_d
# Upsample biasfield to same size as original frame
bias_corr = cv2.resize(bias_corr_d, (nx, ny))
# DEBUG: Flat bias correction
bias_corr = np.ones_like(fr)
return bias_corr
