def get_fmt_correlation(image1, image2, high_pass=0.15):
image1 = get_centered(image1)
image2 = get_centered(image2)
fmt1 = get_fmt(image1, high_pass)
fmt2 = get_fmt(image2, high_pass)
correlation = correlate2d(np.abs(fmt1), np.abs(fmt2)) #magnitude
argmax = np.unravel_index(correlation.argmax(), correlation.shape)
peak = correlation[argmax]
relrow = argmax[0] - correlation.shape[0] / 2.
relcol = argmax[1] - correlation.shape[1] / 2.
return peak, (relrow, relcol)
def get_mask(shape, window, center=None):
"""
Draw a mask of the given shape and window.
If center is not given it will be the shape center.
"""
array = np.zeros(shape)
window2d = radial_extrusion(window)
array_center = shape[0] / 2., shape[1] / 2.
c_row, c_col = array_center
radious = window.shape[0] / 2.
top, bottom = (c_row - radious), (c_row + radious)
left, right = (c_col - radious), (c_col + radious)
try:
array[top:bottom, left:right] = window2d
except ValueError:
print center
print array[top:bottom, left:right].shape, window2d.shape
print "array[%d:%d, %d:%d] = [](%s)" % (top, bottom, left, right,
window2d.shape)
raise
if center:
array = get_centered(array, center, reverse=True)
return array
def get_mask(shape, window, center=None):
"""
Draw a mask of the given shape and window.
If center is not given it will be the shape center.
"""
array = np.zeros(shape)
window2d = radial_extrusion(window)
array_center = shape[0] / 2., shape[1] / 2.
c_row, c_col = array_center
radious = window.shape[0] / 2.
top, bottom = (c_row - radious), (c_row + radious)
left, right = (c_col - radious), (c_col + radious)
try:
array[top:bottom, left:right] = window2d
except ValueError:
print center
print array[top:bottom, left:right].shape, window2d.shape
print "array[%d:%d, %d:%d] = [](%s)" % (top, bottom, left, right,
window2d.shape)
raise
if center:
array = get_centered(array, center, reverse=True)
return array
def get_auto_mask(spectrum,
softness=0,
radious_scale=.8,
zero_scale=1.3,
cuttop=0,
centered=True):
"""
Try to filter spurious data out.
"""
shape = spectrum.shape
rows, cols = shape
center = (rows / 2., cols / 2.)
intensity = get_intensity(spectrum)
circles = get_circles(intensity, 3, 50)
circles = sorted([(circle[1], circle[0], circle[2]) for circle in circles
if get_distance(center, circle[0]) > 10])[-2:]
circles = sorted([(circle[1], circle[0], circle[2]) for circle in circles])
virtual_order, real_order = circles
zero_order = (center, 0, 0)
peak_center, peak_height, peak_radious = real_order
peak_radious = min([(abs(shape[0] / 3.5 - peak[2]), peak[2])
for peak in circles])[1]
windowmaker = lambda x: np.kaiser(x, softness)
window = get_holed_window(windowmaker, peak_radious * radious_scale)
mask = get_mask(shape, window, peak_center)
zerowindow = get_holed_window(windowmaker, peak_radious * zero_scale)
zeromask = 1 - get_mask(shape, zerowindow, zero_order[0])
mask *= zeromask
masked_intensity = mask * intensity
cutoff = masked_intensity > (masked_intensity.max() -
masked_intensity.ptp() * cuttop)
mask[cutoff] = 0
masked = mask * spectrum
if centered:
masked = get_centered(masked, peak_center)
mask = get_centered(mask, peak_center)
centered = get_centered(intensity, peak_center)
return mask, masked, centered
def get_auto_mask(spectrum, softness=0, radious_scale=.8, zero_scale=1.3,
cuttop=0, centered=True):
"""
Try to filter spurious data out.
"""
shape = spectrum.shape
rows, cols = shape
center = (rows / 2., cols / 2.)
intensity = get_intensity(spectrum)
circles = get_circles(intensity, 3, 50)
circles = sorted([(circle[1], circle[0], circle[2])
for circle in circles
if get_distance(center, circle[0]) > 10])[-2:]
circles = sorted([(circle[1], circle[0], circle[2])
for circle in circles])
virtual_order, real_order = circles
zero_order = (center, 0, 0)
peak_center, peak_height, peak_radious = real_order
peak_radious = min([(abs(shape[0] / 3.5 - peak[2]), peak[2])
for peak in circles])[1]
windowmaker = lambda x: np.kaiser(x, softness)
window = get_holed_window(windowmaker, peak_radious * radious_scale)
mask = get_mask(shape, window, peak_center)
zerowindow = get_holed_window(windowmaker, peak_radious * zero_scale)
zeromask = 1 - get_mask(shape, zerowindow, zero_order[0])
mask *= zeromask
masked_intensity = mask * intensity
cutoff = masked_intensity > (masked_intensity.max()
- masked_intensity.ptp() * cuttop)
mask[cutoff] = 0
masked = mask * spectrum
if centered:
masked = get_centered(masked, peak_center)
mask = get_centered(mask, peak_center)
centered = get_centered(intensity, peak_center)
return mask, masked, centered
def get_wide_segment(array, startpoint, endpoint):
"""
Returns an array with the values inside of the 2x1 rectangle so that
the centers of the shorter sides are at the start and end point.
"""
row0, col0 = startpoint
row1, col1 = endpoint
drow = row1 - row0
dcol = col1 - col0
hyp = (drow**2 + dcol**2)**.5
center = (row0 + row1) / 2., (col0 + col1) / 2.
centered = get_centered(array, center)
rotated = rotate(centered, np.arctan2(drow, dcol) * 360 / tau, mode='wrap')
rrows, rcols = rotated.shape
rrowc = rrows / 2.
rcolc = rcols / 2.
col0 = rcolc - hyp / 2.
col1 = rcolc + hyp / 2.
croped = rotated[rrowc - hyp / 2:rrowc + hyp / 2, col0:col1]
sumed = croped.sum(0)
return sumed
def get_wide_segment(array, startpoint, endpoint):
"""
Returns an array with the values inside of the 2x1 rectangle so that
the centers of the shorter sides are at the start and end point.
"""
row0, col0 = startpoint
row1, col1 = endpoint
drow = row1 - row0
dcol = col1 - col0
hyp = (drow ** 2 + dcol ** 2) ** .5
center = (row0 + row1) / 2., (col0 + col1) / 2.
centered = get_centered(array, center)
rotated = rotate(centered, np.arctan2(drow, dcol) * 360 / tau, mode='wrap')
rrows, rcols = rotated.shape
rrowc = rrows / 2.
rcolc = rcols / 2.
col0 = rcolc - hyp / 2.
col1 = rcolc + hyp / 2.
croped = rotated[rrowc - hyp / 2: rrowc + hyp / 2, col0:col1]
sumed = croped.sum(0)
return sumed
