def test_subpixel(fig=None):
np.random.seed(2019)
ims = np.random.rand(40, )**2 + 1e1
smooth_ims = smoothImage(ims)
mpos = peak_local_max(smooth_ims, min_distance=3).flatten()
subpos, subval = subpixelmax(smooth_ims, mpos)
np.testing.assert_array_almost_equal(
subpos,
np.array([34.48945739, 19.7971219, 14.04429215, 7.17665281]),
decimal=8)
np.testing.assert_array_almost_equal(
subval,
np.array([10.75670888, 10.46787185, 10.59470934, 10.70051573]),
decimal=8)
if fig:
plt.figure(fig)
plt.clf()
plt.plot(np.arange(ims.size), ims, '.:r', label='data points')
plt.plot(mpos, ims[mpos], 'om', label='integer maxima')
plt.plot(subpos,
subval,
'.g',
markersize=15,
label='subpixel maxima')
plt.legend(numpoints=1)
plt.close('all')
def fitBackground(im, smooth=True, fig=None, order=3, verbose=1, removeoutliers=False, returndict=None):
""" Fit smooth background to 1D or 2D image
Args:
im (array): input image
Returns
vv (array): estimated background
"""
kk = len(im.shape)
im = np.array(im)
ny = im.shape[0]
ydata0 = np.arange(0., ny)
is1d = kk == 1
if kk > 1:
# 2d signal
nx = im.shape[1]
xdata0 = np.arange(0., nx)
else:
# 1D signal
xdata0 = [0.]
xx, yy = np.meshgrid(xdata0, ydata0)
if smooth:
ims = smoothImage(im)
else:
ims = im
if verbose:
print('fitBackground: is1d %d, order %d' % (is1d, order))
xxf = xx.flatten()
yyf = yy.flatten()
imsf = ims.flatten()
s2d = polyfit2d(xxf, yyf, imsf, order=order)
vv = polyval2d(xx, yy, s2d)
if removeoutliers:
ww = im - vv
gidx = np.abs(ims.flatten() - vv.flatten()) < ww.std()
if verbose:
print('fitBackGround: inliers %d/%d (std %.2f)' %
(gidx.sum(), gidx.size, ww.std()))
s2d = polyfit2d(xxf[gidx], yyf[gidx], imsf[gidx], order=order)
vv = polyval2d(xx, yy, s2d)
if not fig is None:
if kk == 1:
plt.figure(fig)
plt.clf()
plt.subplot(2, 1, 1)
plt.plot(im, '.b', label='signal')
plt.plot(ims, 'og')
# plt.plot(ims, '.c', label='signal');
plt.plot(vv, '.-r', label='background')
# plt.axis('image')
plt.legend()
plt.title('fitBackground: image')
plt.subplot(2, 1, 2)
plt.plot(ww, '.m')
plt.title('Diff plot')
else:
plt.figure(fig)
plt.clf()
plt.subplot(3, 1, 1)
plt.imshow(im, interpolation='nearest')
plt.axis('image')
plt.title('fitBackground: image')
plt.subplot(3, 1, 2)
plt.imshow(vv, interpolation='nearest')
plt.axis('image')
plt.title('fitBackground: interpolation')
plt.subplot(3, 1, 3)
plt.imshow(im - vv, interpolation='nearest')
plt.axis('image')
plt.title('fitBackground: difference')
if not returndict is None:
warnings.warn('please do not use this feature any more')
returndict['xx'] = xx
returndict['yy'] = yy
returndict['ims'] = ims
return vv
def findCrossTemplate(imx,
ksize=31,
fig=None,
istep=2,
verbose=1,
widthmv=6,
lenmv=20.,
sepmv=2.0,
dy=5):
""" Find crosses in image using template match
Arguments
---------
istep : float
sampling rate in mV/pixel
widthmv, lenmv, sepmv : float
parameters of the cross model
Returns
-------
ptsim : array
fitted points
rr : numpy array
response of the filter
results : dict
more results
"""
samplesize = np.array([ksize, ksize + dy])
param = [
None, None, sepmv / istep, 3 * np.pi / 8, -7 * np.pi / 8,
11 * np.pi / 8, np.pi / 8
]
modelpatch, _ = createCross(param,
samplesize,
w=widthmv / istep,
l=lenmv / istep,
lsegment=lenmv / istep,
H=100)
imtmp = pmatlab.setregion(scaleImage(imx), scaleImage(modelpatch), [0, 0])
#rr=cv2.matchTemplate(imx, modelpatch.astype(np.float32), method=cv2.TM_SQDIFF)
rr = cv2.matchTemplate(scaleImage(imx),
scaleImage(modelpatch.astype(imx.dtype)),
method=cv2.TM_CCORR_NORMED)
#rr=cv2.matchTemplate(scaleImage(imx), scaleImage(modelpatch.astype(np.float32)), method=cv2.TM_SQDIFF); rr=-rr
rr = smoothImage(rr)
thr = .65 * rr.max() + .35 * rr.mean()
pts = localMaxima(rr, thr=thr, radius=10 / istep)
pts = np.array(pts)
pts = pts[[1, 0], :]
ptsim = pts + ((samplesize - 1.) / 2).reshape((2, 1))
if verbose:
print('findCrossTemplate: threshold: %.1f, %d local maxima' %
(thr, pts.shape[1]))
if fig is not None:
showIm(imtmp, fig=fig)
plt.plot(ptsim[0], ptsim[1], '.m', markersize=22)
showIm(rr, fig=fig + 1)
plt.colorbar()
plt.title('Template and image')
plt.plot(pts[0], pts[1], '.m', markersize=22)
plt.title('Template match')
qtt.pgeometry.tilefigs([fig, fig + 1])
return ptsim, rr, dict({'modelpatch': modelpatch})
