def process(self, prj, res1, res2):
delta_t, velocity_pix, tol_pix = self.get_velocity_resolution(
prj, res1, res2)
max_bounds = max(self.bounds)
if self.verbose:
print "Processing:", res1.get_epoch(), res2.get_epoch(
), delta_t, velocity_pix, tol_pix
if self.debug >= 2:
fig, ax_check0 = self.stack.add_subplots("Segments check 1",
ncols=1)
fig, ax_check1 = self.stack.add_subplots("Segments check 2",
ncols=1)
epoch_all_mean_ncc = []
for segments1, segments2 in zip(res1, res2):
if self.debug >= 2:
imshow_segmented_image(ax_check0,
segments1,
alpha=1,
projection=prj)
imshow_segmented_image(ax_check1,
segments2,
alpha=1,
projection=prj)
scale = segments1.get_scale()
# print "Scale %s: %s" % (scale, len(segments1))
all_ncc = []
all_ncc_shape = None
for segment1 in segments1:
x_dir = self.x_dir
if x_dir == 'position_angle':
x_dir = prj.p2s(p2i(segment1.get_coord()))
elif nputils.is_callable(x_dir):
x_dir = self.x_dir(prj.p2s(p2i(segment1.get_coord())))
if self.mode == 'ncc_peaks_direct':
region1 = segment1.get_image_region()
if min(region1.get_region().shape) <= 3:
continue
if self.rnd_pos_shift:
shift = np.random.normal(
0,
self.rnd_pos_factor *
segment1.get_coord_error(min_snr=3))
region1.set_shift(shift)
i1 = segments2.get_img().get_data()
# i1 = nputils.shift2d(i1, np.array([4 / velocity_pix] * 2))
i1 = nputils.zoom(i1, region1.get_center(),
[2 * tol_pix, 2 * tol_pix])
i2 = region1.get_region()
shape = [
self.factor * (self.bounds[0] + self.bounds[1]),
self.factor * (self.bounds[2] + self.bounds[3])
]
data = np.zeros(shape)
# print i1.shape, i2.shape
ncc = nputils.norm_xcorr2(i1, i2, mode='same')
peaks = nputils.find_peaks(ncc,
4,
self.ncc_threshold,
fit_gaussian=False)
if len(peaks) > 0:
delta_pix = p2i(peaks - np.array(ncc.shape) / 2)
delta = prj.pixel_scales() * np.array(
delta_pix) * prj.unit
v = delta / self.__get_delta_time(delta_t)
if self.vel_trans:
v = self.vel_trans(
prj.p2s(p2i(segment1.get_coord())), v.T).T
if x_dir is not None:
vx, vy = nputils.vector_projection(v.T,
x_dir) * v.unit
else:
vx, vy = v.T
vx = vx.to(self.unit).value
vy = vy.to(self.unit).value
d = np.array([vx, vy])
if len(vx) > 0:
ix = self.factor * (self.bounds[0] + vx)
iy = self.factor * (self.bounds[2] + vy)
widths = np.array(
[[4 * self.factor * velocity_pix] * 2] *
len(peaks))
# widths = np.array([[1] * 2] * len(peaks))
centers = np.array([iy, ix]).T
heights = ncc[tuple(
np.array([tuple(k) for k in peaks]).T)]
# print widths, centers, heights
data = imgutils.multiple_gaussian(
shape, heights, widths, centers)
ncc = data
else:
ncc = self.ncc_segment(segment1, segments2, 2 * tol_pix)
if ncc is not None:
# print ncc.shape, nputils.coord_max(ncc)
ncc = zoom(ncc, velocity_pix * self.factor, order=3)
# zoom will shift the center
ncc = nputils.shift2d(
ncc, [-(velocity_pix * self.factor) / 2.] * 2)
# print ncc.shape, nputils.coord_max(ncc), velocity_pix * self.factor
ncc = nputils.zoom(ncc,
np.array(ncc.shape) / 2, [
2 * max_bounds * self.factor,
2 * max_bounds * self.factor
])
# print ncc.shape, nputils.coord_max(ncc)
# ncc = ncc[self.factor * (max_bounds - self.bounds[0]):self.factor * (max_bounds + self.bounds[1]),
# self.factor * (max_bounds - self.bounds[2]):self.factor * (max_bounds + self.bounds[3])]
# print ncc.shape
if x_dir is not None:
angle_rad = -np.arctan2(x_dir[1], x_dir[0])
ncc = rotate(ncc,
-angle_rad / (2 * np.pi) * 360,
reshape=False,
order=2)
if ncc is not None:
if self.debug >= 3:
fig, (ax0, ax1, ax2) = self.stack.add_subplots(
"Segment %s" % segment1.get_segmentid(), ncols=3)
r1 = segment1.get_image_region()
ax0.imshow(r1.get_region())
i2 = nputils.zoom(segments2.get_img().get_data(),
r1.get_center(),
[2 * tol_pix, 2 * tol_pix])
ax1.imshow(i2,
norm=plotutils.LogNorm(),
extent=(-tol_pix, tol_pix, -tol_pix,
tol_pix))
plotutils.img_axis(ax1)
ax2.imshow(ncc, extent=self.global_ncc_extent)
plotutils.img_axis(ax2)
all_ncc.append(ncc)
if all_ncc_shape is None:
all_ncc_shape = ncc.shape
if len(all_ncc) > 0:
if scale not in self.global_ncc_scales:
self.global_ncc_scales[scale] = []
self.global_ncc_scales_n[scale] = 0
mean_ncc = self.agg_fct(np.array(all_ncc), axis=0)
epoch_all_mean_ncc.append(mean_ncc)
if self.debug >= 1:
fig, ax = self.stack.add_subplots(
"Ncc epoch %s vs %s scale %s" %
(res1.get_epoch(), res2.get_epoch(), scale))
ax.imshow(mean_ncc, extent=self.global_ncc_extent)
plotutils.img_axis(ax)
self.global_ncc_scales[scale].append(mean_ncc)
self.global_ncc_scales_n[scale] += len(all_ncc)
if self.debug >= 0.5 and len(epoch_all_mean_ncc) > 0:
epoch_mean_ncc = self.agg_fct(np.array(epoch_all_mean_ncc), axis=0)
fig, ax = self.stack.add_subplots(
"Ncc epoch %s vs %s" % (res1.get_epoch(), res2.get_epoch()))
ax.imshow(epoch_mean_ncc, extent=self.global_ncc_extent)
plotutils.img_axis(ax)
def __init__(self):
data = [
[
"unit", u.mas / u.year, "Velocity unit",
nputils.validator_is(u.Unit)
],
[
"bounds", [1, 1, 1, 1], "Velocity bounds",
nputils.validator_list(4, (int, float))
],
[
"filter1", None, "Filter for the first image",
nputils.validator_is((NoneType, nputils.AbstractFilter))
],
[
"filter2", None, "Filter for the second image",
nputils.validator_is((NoneType, nputils.AbstractFilter))
],
[
"tol_pix_range", [4, 25],
"Allowed range of pixel velocity resolution",
nputils.validator_list(2, int)
],
[
"ncc_threshold", 0.6, "Threshold for the NCC",
nputils.validator_is(bool)
],
[
"factor", 10, "Zoom factor of the resulting map",
nputils.validator_in_range(1, 20)
],
[
"method", 'ncc_peaks_direct', "Method used to compute the SCC",
lambda v: v in ['ncc', 'ncc_peaks', 'ncc_peaks_direct']
],
[
"vector_direction", None,
"Project the result on this direction",
lambda v: v == 'position_angle' or nputils.is_callable(
v) or isinstance(v, (list, np.ndarray, NoneType))
],
[
"velocity_trans", None,
"Do any transform on the velocity vector, pre projection",
lambda v: nputils.is_callable(v)
],
[
"rnd_pos_shift", False, "Randomly shift the segments position",
nputils.validator_is((bool, NoneType))
],
[
"rnd_pos_factor", 1.5,
"Factor of the standart deviation of the shift",
nputils.validator_in_range(0.1, 5)
],
[
"img_rnd_shift", 0, "Randomly shift the images (pixel std)",
nputils.validator_in_range(0, 10)
],
[
"shuffle", False, "Suffle the list of images",
nputils.validator_is(bool)
],
]
nputils.BaseConfiguration.__init__(
self, data, title="Stack cross correlation configuration")
def __init__(self):
data = [
["unit", u.mas / u.year, "Velocity unit", nputils.validator_is(u.Unit)],
["bounds", [1, 1, 1, 1], "Velocity bounds", nputils.validator_list(4, (int, float))],
["filter1", None, "Filter for the first image", nputils.validator_is((NoneType, nputils.AbstractFilter))],
["filter2", None, "Filter for the second image", nputils.validator_is((NoneType, nputils.AbstractFilter))],
["tol_pix_range", [4, 25], "Allowed range of pixel velocity resolution", nputils.validator_list(2, int)],
["ncc_threshold", 0.6, "Threshold for the NCC", nputils.validator_is(bool)],
["factor", 10, "Zoom factor of the resulting map", nputils.validator_in_range(1, 20)],
["method", 'ncc_peaks_direct', "Method used to compute the SCC", lambda v: v in ['ncc', 'ncc_peaks', 'ncc_peaks_direct']],
["vector_direction", None, "Project the result on this direction", lambda v: v == 'position_angle' or nputils.is_callable(v) or isinstance(v, (list, np.ndarray, NoneType))],
["velocity_trans", None, "Do any transform on the velocity vector, pre projection", lambda v: nputils.is_callable(v)],
["rnd_pos_shift", False, "Randomly shift the segments position", nputils.validator_is((bool, NoneType))],
["rnd_pos_factor", 1.5, "Factor of the standart deviation of the shift", nputils.validator_in_range(0.1, 5)],
["img_rnd_shift", 0, "Randomly shift the images (pixel std)", nputils.validator_in_range(0, 10)],
["shuffle", False, "Suffle the list of images", nputils.validator_is(bool)],
]
nputils.BaseConfiguration.__init__(self, data, title="Stack cross correlation configuration")
def plot_links_map(ax, img, projection, links, color_style='link', mode='com', colorbar_setting=None,
map_cmap='jet', vector_width=4, link_id_label=False, num_bbox=None,
ang_vel_arrows=False, ang_vel_unit=u.mas / u.year, pix_per_ang_vel_unit=1, **kwargs):
"""Display features links on a map.
Parameters
----------
ax : :class:`matplotlib.axes.Axes`
img : Image
An image to be used as background map.
projection : :class:`libwise.imgutils.Projection`
links : a list of :class:`wise.matcher.FeaturesLink`
color_style : str, optional
'link': one color per link.
'date': map the features epochs to a color.
any color string: use one color for each displacements vectors.
function: a function that take a feature as argument and return a color.
mode : str, optional
Coord mode for the location of the features: 'lm', 'com' or 'cos'.
colorbar_setting : ColorBar, optional
Settings for the color bar if color_style is 'date'.
map_cmap : :class:`matplotlib.cm.ColorMap` or str, optional
vector_width : int, optional
Width of the displacements vector arrows. Default is 4.
link_id_label : bool, optional
Annotate the links with there ids.
num_bbox : dict, optional
**kwargs:
Additional arguments to be passed to :func:`libwise.plotutils.imshow_image`.
.. _tags: plt_matching
"""
color_fct = None
if color_style == 'date':
all_epochs = matcher.get_all_epochs(links)
epochs_map = plotutils.build_epochs_mappable(all_epochs, colorbar_setting.get_cmap())
if colorbar_setting is None:
colorbar_setting = plotutils.ColorbarSetting(ticks_locator=mdates.AutoDateLocator(),
ticks_formatter=mdates.DateFormatter('%m/%y'))
colorbar_setting.add_colorbar(epochs_map, ax)
color_fct = lambda f: epochs_map.to_rgba(f.get_epoch())
elif color_style is not 'link' and isinstance(color_style, str):
color_fct = lambda f: color_style
elif nputils.is_callable(color_style):
color_fct = color_style
plotutils.imshow_image(ax, img, projection=projection, title=False, cmap=plotutils.get_cmap(map_cmap), **kwargs)
for link in links:
delta_info = link.get_delta_info(measured_delta=False)
if ang_vel_arrows:
plot_velocity_vector(ax, delta_info, projection, ang_vel_unit, pix_per_ang_vel_unit,
color_fct=color_fct, mode=mode, lw=0.5,
fc=link.get_color(), ec='k', alpha=0.9, zorder=link.size(), width=vector_width)
else:
plot_displacement_vector(ax, delta_info, color_fct=color_fct, mode=mode, lw=0.5,
fc=link.get_color(), ec='k', alpha=0.9, zorder=link.size(), width=vector_width)
# y, x = link.get_features()[int(np.random.normal(s / 2, s / 4))].get_coord()
if link_id_label:
y, x = link.last().get_coord()
if num_bbox is None:
props = dict(boxstyle='round', facecolor='wheat', alpha=0.7, edgecolor=link.get_color(), lw=1.5)
ax.text(x, y, link.get_id(), bbox=num_bbox, zorder=200, size='small')
def process(self, prj, res1, res2):
delta_t, velocity_pix, tol_pix = self.get_velocity_resolution(prj, res1, res2)
max_bounds = max(self.bounds)
if self.verbose:
print "Processing:", res1.get_epoch(), res2.get_epoch(), delta_t, velocity_pix, tol_pix
if self.debug >= 2:
fig, ax_check0 = self.stack.add_subplots("Segments check 1", ncols=1)
fig, ax_check1 = self.stack.add_subplots("Segments check 2", ncols=1)
epoch_all_mean_ncc = []
for segments1, segments2 in zip(res1, res2):
if self.debug >= 2:
imshow_segmented_image(ax_check0, segments1, alpha=1, projection=prj)
imshow_segmented_image(ax_check1, segments2, alpha=1, projection=prj)
scale = segments1.get_scale()
# print "Scale %s: %s" % (scale, len(segments1))
all_ncc = []
all_ncc_shape = None
for segment1 in segments1:
x_dir = self.x_dir
if x_dir == 'position_angle':
x_dir = prj.p2s(p2i(segment1.get_coord()))
elif nputils.is_callable(x_dir):
x_dir = self.x_dir(prj.p2s(p2i(segment1.get_coord())))
if self.mode == 'ncc_peaks_direct':
region1 = segment1.get_image_region()
if min(region1.get_region().shape) <= 3:
continue
if self.rnd_pos_shift:
shift = np.random.normal(0, self.rnd_pos_factor * segment1.get_coord_error(min_snr=3))
region1.set_shift(shift)
i1 = segments2.get_img().get_data()
# i1 = nputils.shift2d(i1, np.array([4 / velocity_pix] * 2))
i1 = nputils.zoom(i1, region1.get_center(), [2 * tol_pix, 2 * tol_pix])
i2 = region1.get_region()
shape = [self.factor * (self.bounds[0] + self.bounds[1]), self.factor * (self.bounds[2] + self.bounds[3])]
data = np.zeros(shape)
# print i1.shape, i2.shape
ncc = nputils.norm_xcorr2(i1, i2, mode='same')
peaks = nputils.find_peaks(ncc, 4, self.ncc_threshold, fit_gaussian=False)
if len(peaks) > 0:
delta_pix = p2i(peaks - np.array(ncc.shape) / 2)
delta = prj.pixel_scales() * np.array(delta_pix) * prj.unit
v = delta / self.__get_delta_time(delta_t)
if self.vel_trans:
v = self.vel_trans(prj.p2s(p2i(segment1.get_coord())), v.T).T
if x_dir is not None:
vx, vy = nputils.vector_projection(v.T, x_dir) * v.unit
else:
vx, vy = v.T
vx = vx.to(self.unit).value
vy = vy.to(self.unit).value
d = np.array([vx, vy])
if len(vx) > 0:
ix = self.factor * (self.bounds[0] + vx)
iy = self.factor * (self.bounds[2] + vy)
widths = np.array([[4 * self.factor * velocity_pix] * 2] * len(peaks))
# widths = np.array([[1] * 2] * len(peaks))
centers = np.array([iy, ix]).T
heights = ncc[tuple(np.array([tuple(k) for k in peaks]).T)]
# print widths, centers, heights
data = imgutils.multiple_gaussian(shape, heights, widths, centers)
ncc = data
else:
ncc = self.ncc_segment(segment1, segments2, 2 * tol_pix)
if ncc is not None:
# print ncc.shape, nputils.coord_max(ncc)
ncc = zoom(ncc, velocity_pix * self.factor, order=3)
# zoom will shift the center
ncc = nputils.shift2d(ncc, [- (velocity_pix * self.factor) / 2.] * 2)
# print ncc.shape, nputils.coord_max(ncc), velocity_pix * self.factor
ncc = nputils.zoom(ncc, np.array(ncc.shape) / 2, [2 * max_bounds * self.factor, 2 * max_bounds * self.factor])
# print ncc.shape, nputils.coord_max(ncc)
# ncc = ncc[self.factor * (max_bounds - self.bounds[0]):self.factor * (max_bounds + self.bounds[1]),
# self.factor * (max_bounds - self.bounds[2]):self.factor * (max_bounds + self.bounds[3])]
# print ncc.shape
if x_dir is not None:
angle_rad = - np.arctan2(x_dir[1], x_dir[0])
ncc = rotate(ncc, - angle_rad / (2 * np.pi) * 360, reshape=False, order=2)
if ncc is not None:
if self.debug >= 3:
fig, (ax0, ax1, ax2) = self.stack.add_subplots("Segment %s" % segment1.get_segmentid(), ncols=3)
r1 = segment1.get_image_region()
ax0.imshow(r1.get_region())
i2 = nputils.zoom(segments2.get_img().get_data(), r1.get_center(), [2 * tol_pix, 2 * tol_pix])
ax1.imshow(i2, norm=plotutils.LogNorm(),
extent=(-tol_pix, tol_pix, -tol_pix, tol_pix))
plotutils.img_axis(ax1)
ax2.imshow(ncc, extent=self.global_ncc_extent)
plotutils.img_axis(ax2)
all_ncc.append(ncc)
if all_ncc_shape is None:
all_ncc_shape = ncc.shape
if len(all_ncc) > 0:
if scale not in self.global_ncc_scales:
self.global_ncc_scales[scale] = []
self.global_ncc_scales_n[scale] = 0
mean_ncc = self.agg_fct(np.array(all_ncc), axis=0)
epoch_all_mean_ncc.append(mean_ncc)
if self.debug >= 1:
fig, ax = self.stack.add_subplots("Ncc epoch %s vs %s scale %s" % (res1.get_epoch(), res2.get_epoch(), scale))
ax.imshow(mean_ncc, extent=self.global_ncc_extent)
plotutils.img_axis(ax)
self.global_ncc_scales[scale].append(mean_ncc)
self.global_ncc_scales_n[scale] += len(all_ncc)
if self.debug >= 0.5 and len(epoch_all_mean_ncc) > 0:
epoch_mean_ncc = self.agg_fct(np.array(epoch_all_mean_ncc), axis=0)
fig, ax = self.stack.add_subplots("Ncc epoch %s vs %s" % (res1.get_epoch(), res2.get_epoch()))
ax.imshow(epoch_mean_ncc, extent=self.global_ncc_extent)
plotutils.img_axis(ax)
