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gui.py
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gui.py
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import SimpleITK as sitk
import matplotlib.pyplot as plt
import ipywidgets as widgets
from IPython.display import display
import numpy as np
from matplotlib.widgets import RectangleSelector
import matplotlib.patches as patches
import matplotlib.cm as cm
from matplotlib.ticker import MaxNLocator
import copy
class RegistrationPointDataAquisition(object):
"""
This class provides a GUI for localizing corresponding points in two images, and for evaluating registration results using a linked cursor
approach, user clicks in one image and the corresponding point is added to the other image.
"""
def __init__(
self,
fixed_image,
moving_image,
fixed_window_level=None,
moving_window_level=None,
figure_size=(10, 8),
known_transformation=None,
):
self.fixed_image = fixed_image
(
self.fixed_npa,
self.fixed_min_intensity,
self.fixed_max_intensity,
) = self.get_window_level_numpy_array(self.fixed_image, fixed_window_level)
self.moving_image = moving_image
(
self.moving_npa,
self.moving_min_intensity,
self.moving_max_intensity,
) = self.get_window_level_numpy_array(self.moving_image, moving_window_level)
self.fixed_point_indexes = []
self.moving_point_indexes = []
self.click_history = (
[]
) # Keep a history of user point localizations, enabling undo of last localization.
self.known_transformation = known_transformation # If the transformation is valid (not None) then corresponding points are automatically added.
self.text_and_marker_color = "red"
ui = self.create_ui()
display(ui)
# Create a figure with two axes for the fixed and moving images.
self.fig, axes = plt.subplots(1, 2, figsize=figure_size)
# self.fig.canvas.set_window_title('Registration Points Acquisition')
self.fixed_axes = axes[0]
self.moving_axes = axes[1]
# Connect the mouse button press to the canvas (__call__ method is the invoked callback).
self.fig.canvas.mpl_connect("button_press_event", self)
# Display the data and the controls, first time we display the images is outside the "update_display" method
# as that method relies on the previous zoom factor which doesn't exist yet.
self.fixed_axes.imshow(
self.fixed_npa[self.fixed_slider.value, :, :]
if self.fixed_slider
else self.fixed_npa,
cmap=plt.cm.Greys_r,
vmin=self.fixed_min_intensity,
vmax=self.fixed_max_intensity,
)
self.moving_axes.imshow(
self.moving_npa[self.moving_slider.value, :, :]
if self.moving_slider
else self.moving_npa,
cmap=plt.cm.Greys_r,
vmin=self.moving_min_intensity,
vmax=self.moving_max_intensity,
)
self.update_display()
def create_ui(self):
# Create the active UI components. Height and width are specified in 'em' units. This is
# a html size specification, size relative to current font size.
self.viewing_checkbox = widgets.RadioButtons(
description="Interaction mode:", options=["edit", "view"], value="edit"
)
self.clearlast_button = widgets.Button(
description="Clear Last", width="7em", height="3em"
)
self.clearlast_button.on_click(self.clear_last)
self.clearall_button = widgets.Button(
description="Clear All", width="7em", height="3em"
)
self.clearall_button.on_click(self.clear_all)
# Sliders are only created if a 3D image, otherwise no need.
self.fixed_slider = self.moving_slider = None
if self.fixed_npa.ndim == 3:
self.fixed_slider = widgets.IntSlider(
description="fixed image z slice:",
min=0,
max=self.fixed_npa.shape[0] - 1,
step=1,
value=int((self.fixed_npa.shape[0] - 1) / 2),
width="20em",
)
self.fixed_slider.observe(self.on_slice_slider_value_change, names="value")
self.moving_slider = widgets.IntSlider(
description="moving image z slice:",
min=0,
max=self.moving_npa.shape[0] - 1,
step=1,
value=int((self.moving_npa.shape[0] - 1) / 2),
width="19em",
)
self.moving_slider.observe(self.on_slice_slider_value_change, names="value")
bx0 = widgets.Box(
padding=7, children=[self.fixed_slider, self.moving_slider]
)
# Layout of UI components. This is pure ugliness because we are not using a UI toolkit. Layout is done
# using the box widget and padding so that the visible UI components are spaced nicely.
bx1 = widgets.Box(padding=7, children=[self.viewing_checkbox])
bx2 = widgets.Box(padding=15, children=[self.clearlast_button])
bx3 = widgets.Box(padding=15, children=[self.clearall_button])
return (
widgets.HBox(children=[widgets.HBox(children=[bx1, bx2, bx3]), bx0])
if self.fixed_npa.ndim == 3
else widgets.HBox(children=[widgets.HBox(children=[bx1, bx2, bx3])])
)
def get_window_level_numpy_array(self, image, window_level):
"""
Get the numpy array representation of the image and the min and max of the intensities
used for display.
"""
npa = sitk.GetArrayViewFromImage(image)
if not window_level:
return npa, npa.min(), npa.max()
else:
return (
npa,
window_level[1] - window_level[0] / 2.0,
window_level[1] + window_level[0] / 2.0,
)
def on_slice_slider_value_change(self, change):
self.update_display()
def update_display(self):
"""
Display the two images based on the slider values, if relevant, and the points which are on the
displayed slices.
"""
# We want to keep the zoom factor which was set prior to display, so we log it before
# clearing the axes.
fixed_xlim = self.fixed_axes.get_xlim()
fixed_ylim = self.fixed_axes.get_ylim()
moving_xlim = self.moving_axes.get_xlim()
moving_ylim = self.moving_axes.get_ylim()
# Draw the fixed image in the first subplot and the localized points.
self.fixed_axes.clear()
self.fixed_axes.imshow(
self.fixed_npa[self.fixed_slider.value, :, :]
if self.fixed_slider
else self.fixed_npa,
cmap=plt.cm.Greys_r,
vmin=self.fixed_min_intensity,
vmax=self.fixed_max_intensity,
)
# Positioning the text is a bit tricky, we position relative to the data coordinate system, but we
# want to specify the shift in pixels as we are dealing with display. We therefore (a) get the data
# point in the display coordinate system in pixel units (b) modify the point using pixel offset and
# transform back to the data coordinate system for display.
text_x_offset = -10
text_y_offset = -10
for i, pnt in enumerate(self.fixed_point_indexes):
if (
self.fixed_slider and int(pnt[2] + 0.5) == self.fixed_slider.value
) or not self.fixed_slider:
self.fixed_axes.scatter(
pnt[0], pnt[1], s=90, marker="+", color=self.text_and_marker_color
)
# Get point in pixels.
text_in_data_coords = self.fixed_axes.transData.transform(
[pnt[0], pnt[1]]
)
# Offset in pixels and get in data coordinates.
text_in_data_coords = self.fixed_axes.transData.inverted().transform(
(
text_in_data_coords[0] + text_x_offset,
text_in_data_coords[1] + text_y_offset,
)
)
self.fixed_axes.text(
text_in_data_coords[0],
text_in_data_coords[1],
str(i),
color=self.text_and_marker_color,
)
self.fixed_axes.set_title(
f"fixed image - localized {len(self.fixed_point_indexes)} points"
)
self.fixed_axes.set_axis_off()
# Draw the moving image in the second subplot and the localized points.
self.moving_axes.clear()
self.moving_axes.imshow(
self.moving_npa[self.moving_slider.value, :, :]
if self.moving_slider
else self.moving_npa,
cmap=plt.cm.Greys_r,
vmin=self.moving_min_intensity,
vmax=self.moving_max_intensity,
)
for i, pnt in enumerate(self.moving_point_indexes):
if (
self.moving_slider and int(pnt[2] + 0.5) == self.moving_slider.value
) or not self.moving_slider:
self.moving_axes.scatter(
pnt[0], pnt[1], s=90, marker="+", color=self.text_and_marker_color
)
text_in_data_coords = self.moving_axes.transData.transform(
[pnt[0], pnt[1]]
)
text_in_data_coords = self.moving_axes.transData.inverted().transform(
(
text_in_data_coords[0] + text_x_offset,
text_in_data_coords[1] + text_y_offset,
)
)
self.moving_axes.text(
text_in_data_coords[0],
text_in_data_coords[1],
str(i),
color=self.text_and_marker_color,
)
self.moving_axes.set_title(
f"moving image - localized {len(self.moving_point_indexes)} points"
)
self.moving_axes.set_axis_off()
# Set the zoom factor back to what it was before we cleared the axes, and rendered our data.
self.fixed_axes.set_xlim(fixed_xlim)
self.fixed_axes.set_ylim(fixed_ylim)
self.moving_axes.set_xlim(moving_xlim)
self.moving_axes.set_ylim(moving_ylim)
self.fig.canvas.draw_idle()
def clear_all(self, button):
"""
Get rid of all the data.
"""
del self.fixed_point_indexes[:]
del self.moving_point_indexes[:]
del self.click_history[:]
self.update_display()
def clear_last(self, button):
"""
Remove last point or point-pair addition (depends on whether the interface is used for localizing point pairs or
evaluation of registration).
"""
if self.click_history:
if self.known_transformation:
self.click_history.pop().pop()
self.click_history.pop().pop()
self.update_display()
def get_points(self):
"""
Get the points in the image coordinate systems.
"""
if len(self.fixed_point_indexes) != len(self.moving_point_indexes):
raise Exception(
"Number of localized points in fixed and moving images does not match."
)
fixed_point_list = [
self.fixed_image.TransformContinuousIndexToPhysicalPoint(pnt)
for pnt in self.fixed_point_indexes
]
moving_point_list = [
self.moving_image.TransformContinuousIndexToPhysicalPoint(pnt)
for pnt in self.moving_point_indexes
]
return fixed_point_list, moving_point_list
def __call__(self, event):
"""
Callback invoked when the user clicks inside the figure.
"""
# We add points only in 'edit' mode. If the spatial transformation between the two images is known, self.known_transformation was set,
# then every button_press_event will generate a point in each of the images. Finally, we enforce that all points have a corresponding
# point in the other image by not allowing the user to add multiple points in the same image, they have to add points by switching between
# the two images.
if self.viewing_checkbox.value == "edit":
if event.inaxes == self.fixed_axes:
if len(self.fixed_point_indexes) - len(self.moving_point_indexes) <= 0:
self.fixed_point_indexes.append(
(event.xdata, event.ydata, self.fixed_slider.value)
if self.fixed_slider
else (event.xdata, event.ydata)
)
self.click_history.append(self.fixed_point_indexes)
if self.known_transformation:
moving_point_physical = self.known_transformation.TransformPoint(
self.fixed_image.TransformContinuousIndexToPhysicalPoint(
self.fixed_point_indexes[-1]
)
)
moving_point_indexes = (
self.moving_image.TransformPhysicalPointToContinuousIndex(
moving_point_physical
)
)
self.moving_point_indexes.append(moving_point_indexes)
self.click_history.append(self.moving_point_indexes)
if self.moving_slider:
z_index = int(moving_point_indexes[2] + 0.5)
if (
self.moving_slider.max >= z_index
and self.moving_slider.min <= z_index
):
self.moving_slider.value = z_index
self.update_display()
if event.inaxes == self.moving_axes:
if len(self.moving_point_indexes) - len(self.fixed_point_indexes) <= 0:
self.moving_point_indexes.append(
(event.xdata, event.ydata, self.moving_slider.value)
if self.moving_slider
else (event.xdata, event.ydata)
)
self.click_history.append(self.moving_point_indexes)
if self.known_transformation:
inverse_transform = self.known_transformation.GetInverse()
fixed_point_physical = inverse_transform.TransformPoint(
self.moving_image.TransformContinuousIndexToPhysicalPoint(
self.moving_point_indexes[-1]
)
)
fixed_point_indexes = (
self.fixed_image.TransformPhysicalPointToContinuousIndex(
fixed_point_physical
)
)
self.fixed_point_indexes.append(fixed_point_indexes)
self.click_history.append(self.fixed_point_indexes)
if self.fixed_slider:
z_index = int(fixed_point_indexes[2] + 0.5)
if (
self.fixed_slider.max >= z_index
and self.fixed_slider.min <= z_index
):
self.fixed_slider.value = z_index
self.update_display()
class PointDataAquisition(object):
def __init__(self, image, window_level=None, figure_size=(10, 8)):
self.image = image
(
self.npa,
self.min_intensity,
self.max_intensity,
) = self.get_window_level_numpy_array(self.image, window_level)
self.point_indexes = []
ui = self.create_ui()
display(ui)
# Create a figure.
self.fig, self.axes = plt.subplots(1, 1, figsize=figure_size)
# Connect the mouse button press to the canvas (__call__ method is the invoked callback).
self.fig.canvas.mpl_connect("button_press_event", self)
# Display the data and the controls, first time we display the image is outside the "update_display" method
# as that method relies on the previous zoom factor which doesn't exist yet.
self.axes.imshow(
self.npa[self.slice_slider.value, :, :] if self.slice_slider else self.npa,
cmap=plt.cm.Greys_r,
vmin=self.min_intensity,
vmax=self.max_intensity,
)
self.update_display()
def create_ui(self):
# Create the active UI components. Height and width are specified in 'em' units. This is
# a html size specification, size relative to current font size.
self.viewing_checkbox = widgets.RadioButtons(
description="Interaction mode:", options=["edit", "view"], value="edit"
)
self.clearlast_button = widgets.Button(
description="Clear Last", width="7em", height="3em"
)
self.clearlast_button.on_click(self.clear_last)
self.clearall_button = widgets.Button(
description="Clear All", width="7em", height="3em"
)
self.clearall_button.on_click(self.clear_all)
# Slider is only created if a 3D image, otherwise no need.
self.slice_slider = None
if self.npa.ndim == 3:
self.slice_slider = widgets.IntSlider(
description="image z slice:",
min=0,
max=self.npa.shape[0] - 1,
step=1,
value=int((self.npa.shape[0] - 1) / 2),
width="20em",
)
self.slice_slider.observe(self.on_slice_slider_value_change, names="value")
bx0 = widgets.Box(padding=7, children=[self.slice_slider])
# Layout of UI components. This is pure ugliness because we are not using a UI toolkit. Layout is done
# using the box widget and padding so that the visible UI components are spaced nicely.
bx1 = widgets.Box(padding=7, children=[self.viewing_checkbox])
bx2 = widgets.Box(padding=15, children=[self.clearlast_button])
bx3 = widgets.Box(padding=15, children=[self.clearall_button])
return (
widgets.HBox(children=[widgets.HBox(children=[bx1, bx2, bx3]), bx0])
if self.slice_slider
else widgets.HBox(children=[widgets.HBox(children=[bx1, bx2, bx3])])
)
def get_window_level_numpy_array(self, image, window_level):
npa = sitk.GetArrayViewFromImage(image)
if not window_level:
return npa, npa.min(), npa.max()
else:
return (
npa,
window_level[1] - window_level[0] / 2.0,
window_level[1] + window_level[0] / 2.0,
)
def on_slice_slider_value_change(self, change):
self.update_display()
def update_display(self):
# We want to keep the zoom factor which was set prior to display, so we log it before
# clearing the axes.
xlim = self.axes.get_xlim()
ylim = self.axes.get_ylim()
# Draw the image and localized points.
self.axes.clear()
self.axes.imshow(
self.npa[self.slice_slider.value, :, :] if self.slice_slider else self.npa,
cmap=plt.cm.Greys_r,
vmin=self.min_intensity,
vmax=self.max_intensity,
)
# Positioning the text is a bit tricky, we position relative to the data coordinate system, but we
# want to specify the shift in pixels as we are dealing with display. We therefore (a) get the data
# point in the display coordinate system in pixel units (b) modify the point using pixel offset and
# transform back to the data coordinate system for display.
text_x_offset = -10
text_y_offset = -10
for i, pnt in enumerate(self.point_indexes):
if (
self.slice_slider and int(pnt[2] + 0.5) == self.slice_slider.value
) or not self.slice_slider:
self.axes.scatter(pnt[0], pnt[1], s=90, marker="+", color="yellow")
# Get point in pixels.
text_in_data_coords = self.axes.transData.transform([pnt[0], pnt[1]])
# Offset in pixels and get in data coordinates.
text_in_data_coords = self.axes.transData.inverted().transform(
(
text_in_data_coords[0] + text_x_offset,
text_in_data_coords[1] + text_y_offset,
)
)
self.axes.text(
text_in_data_coords[0],
text_in_data_coords[1],
str(i),
color="yellow",
)
self.axes.set_title(f"localized {len(self.point_indexes)} points")
self.axes.set_axis_off()
# Set the zoom factor back to what it was before we cleared the axes, and rendered our data.
self.axes.set_xlim(xlim)
self.axes.set_ylim(ylim)
self.fig.canvas.draw_idle()
def add_point_indexes(self, point_index_data):
self.validate_points(point_index_data)
self.point_indexes.append(list(point_index_data))
self.update_display()
def set_point_indexes(self, point_index_data):
self.validate_points(point_index_data)
del self.point_indexes[:]
self.point_indexes = list(point_index_data)
self.update_display()
def validate_points(self, point_index_data):
for p in point_index_data:
if self.npa.ndim != len(p):
raise ValueError(
"Given point ("
+ ", ".join(map(str, p))
+ ") dimension does not match image dimension."
)
outside_2d_bounds = (
p[0] >= self.npa.shape[2]
or p[0] < 0
or p[1] >= self.npa.shape[1]
or p[1] < 0
)
outside_bounds = outside_2d_bounds or (
False if self.npa.ndim == 2 else p[2] >= self.npa.shape[0] or p[2] < 0
)
if outside_bounds:
raise ValueError(
"Given point ("
+ ", ".join(map(str, p))
+ ") is outside the image bounds."
)
def clear_all(self, button):
del self.point_indexes[:]
self.update_display()
def clear_last(self, button):
if self.point_indexes:
self.point_indexes.pop()
self.update_display()
def get_points(self):
return [
self.image.TransformContinuousIndexToPhysicalPoint(pnt)
for pnt in self.point_indexes
]
def get_point_indexes(self):
"""
Return the point indexes, not the continous index we keep.
"""
# Round and then cast to int, just rounding will return a float
return [tuple(map(lambda x: int(round(x)), pnt)) for pnt in self.point_indexes]
def __call__(self, event):
if self.viewing_checkbox.value == "edit":
if event.inaxes == self.axes:
self.point_indexes.append(
(event.xdata, event.ydata, self.slice_slider.value)
if self.slice_slider
else (event.xdata, event.ydata)
)
self.update_display()
def multi_image_display2D(
image_list,
title_list=None,
window_level_list=None,
figure_size=(10, 8),
horizontal=True,
):
if title_list:
if len(image_list) != len(title_list):
raise ValueError("Title list and image list lengths do not match")
else:
title_list = [""] * len(image_list)
# Create a figure.
col_num, row_num = (len(image_list), 1) if horizontal else (1, len(image_list))
fig, axes = plt.subplots(row_num, col_num, figsize=figure_size)
if len(image_list) == 1:
axes = [axes]
# Get images as numpy arrays for display and the window level settings
npa_list = list(map(sitk.GetArrayViewFromImage, image_list))
if not window_level_list:
min_intensity_list = list(map(np.min, npa_list))
max_intensity_list = list(map(np.max, npa_list))
else:
min_intensity_list = list(map(lambda x: x[1] - x[0] / 2.0, window_level_list))
max_intensity_list = list(map(lambda x: x[1] + x[0] / 2.0, window_level_list))
# Draw the image(s)
for ax, npa, title, min_intensity, max_intensity in zip(
axes, npa_list, title_list, min_intensity_list, max_intensity_list
):
ax.imshow(npa, cmap=plt.cm.Greys_r, vmin=min_intensity, vmax=max_intensity)
ax.set_title(title)
ax.set_axis_off()
fig.tight_layout()
return (fig, axes)
class MultiImageDisplay(object):
"""
This class provides a GUI for displaying 3D images. It supports display of
multiple images in the same UI. The image slices are selected according to
the axis specified by the user. Each image can have a title and a slider to
scroll through the stack. The images can also share a single slider if they
have the same number of slices along the given axis. Images are either
grayscale or color. The intensity range used for display (window-level) can
be specified by the user as input to the constructor or set via the displayed
slider. For color images the intensity control slider will be disabled. This
allows us to display both color and grayscale images in the same figure with
a consistent look to the controls. The range of the intensity slider is set
to be from top/bottom 2% of intensities (accommodating for outliers). Images
are displayed either in horizontal or vertical layout, depending on the
users choice.
"""
def __init__(
self,
image_list,
axis=0,
shared_slider=False,
title_list=None,
window_level_list=None,
intensity_slider_range_percentile=[2, 98],
figure_size=(10, 8),
horizontal=True,
):
self.npa_list, wl_range, wl_init = self.get_window_level_numpy_array(
image_list, window_level_list, intensity_slider_range_percentile
)
if title_list:
if len(image_list) != len(title_list):
raise ValueError("Title list and image list lengths do not match")
self.title_list = list(title_list)
else:
self.title_list = [""] * len(image_list)
# Our dynamic slice, based on the axis the user specifies
self.slc = [slice(None)] * 3
self.axis = axis
ui = self.create_ui(shared_slider, wl_range, wl_init)
display(ui)
# Create a figure.
col_num, row_num = (len(image_list), 1) if horizontal else (1, len(image_list))
self.fig, self.axes = plt.subplots(row_num, col_num, figsize=figure_size)
if len(image_list) == 1:
self.axes = [self.axes]
# Display the data and the controls, first time we display the image is outside the "update_display" method
# as that method relies on the previous zoom factor which doesn't exist yet.
for ax, npa, slider, wl_slider in zip(
self.axes, self.npa_list, self.slider_list, self.wl_list
):
self.slc[self.axis] = slice(slider.value, slider.value + 1)
# Need to use squeeze to collapse degenerate dimension (e.g. RGB image size 124 124 1 3)
ax.imshow(
np.squeeze(npa[tuple(self.slc)]),
cmap=plt.cm.Greys_r,
vmin=wl_slider.value[0],
vmax=wl_slider.value[1],
)
self.update_display()
plt.tight_layout()
def create_ui(self, shared_slider, wl_range, wl_init):
# Create the active UI components. Height and width are specified in 'em' units. This is
# a html size specification, size relative to current font size.
if shared_slider:
# Validate that all the images have the same size along the axis which we scroll through
sz = self.npa_list[0].shape[self.axis]
for npa in self.npa_list:
if npa.shape[self.axis] != sz:
raise ValueError(
"Not all images have the same size along the specified axis, cannot share slider."
)
slider = widgets.IntSlider(
description="image slice:",
min=0,
max=sz - 1,
step=1,
value=int((sz - 1) / 2),
width="20em",
)
slider.observe(self.on_slice_slider_value_change, names="value")
self.slider_list = [slider] * len(self.npa_list)
slicer_box = widgets.Box(padding=7, children=[slider])
else:
self.slider_list = []
for npa in self.npa_list:
slider = widgets.IntSlider(
description="image slice:",
min=0,
max=npa.shape[self.axis] - 1,
step=1,
value=int((npa.shape[self.axis] - 1) / 2),
width="20em",
)
slider.observe(self.on_slice_slider_value_change, names="value")
self.slider_list.append(slider)
slicer_box = widgets.Box(padding=7, children=self.slider_list)
self.wl_list = []
# Each image has a window-level slider, but it is disabled if the image
# is a color image len(npa.shape)==4 . This allows us to display both
# color and grayscale images in the same UI while retaining a reasonable
# layout for the sliders.
for r_values, i_values, npa in zip(wl_range, wl_init, self.npa_list):
wl_range_slider = widgets.IntRangeSlider(
description="intensity:",
min=r_values[0],
max=r_values[1],
step=1,
value=[i_values[0], i_values[1]],
width="20em",
disabled=len(npa.shape) == 4,
)
wl_range_slider.observe(self.on_wl_slider_value_change, names="value")
self.wl_list.append(wl_range_slider)
wl_box = widgets.Box(padding=7, children=self.wl_list)
return widgets.VBox(children=[slicer_box, wl_box])
def get_window_level_numpy_array(
self, image_list, window_level_list, intensity_slider_range_percentile
):
# Using GetArray and not GetArrayView because we don't keep references
# to the original images. If they are deleted outside the view would become
# invalid, so we use a copy which guarantees that the GUI is consistent.
npa_list = list(map(sitk.GetArrayFromImage, image_list))
wl_range = []
wl_init = []
# We need to iterate over the images because they can be a mix of
# grayscale and color images. If they are color we set the wl_range
# to [0,255] and the wl_init is equal, ignoring the window_level_list
# entry.
for i, npa in enumerate(npa_list):
if len(npa.shape) == 4: # color image
wl_range.append((0, 255))
wl_init.append((0, 255))
# ignore any window_level_list entry
else:
# We don't necessarily take the minimum/maximum values, just in case there are outliers
# user can specify how much to take off from top and bottom.
min_max = np.percentile(
npa.flatten(), intensity_slider_range_percentile
)
wl_range.append((min_max[0], min_max[1]))
if not window_level_list: # No list was given.
wl_init.append(wl_range[-1])
else:
wl = window_level_list[i]
if wl:
wl_init.append((wl[1] - wl[0] / 2.0, wl[1] + wl[0] / 2.0))
else: # We have a list, but for this image the entry was left empty: []
wl_init.append(wl_range[-1])
return (npa_list, wl_range, wl_init)
def on_slice_slider_value_change(self, change):
self.update_display()
def on_wl_slider_value_change(self, change):
self.update_display()
def update_display(self):
# Draw the image(s)
for ax, npa, title, slider, wl_slider in zip(
self.axes, self.npa_list, self.title_list, self.slider_list, self.wl_list
):
# We want to keep the zoom factor which was set prior to display, so we log it before
# clearing the axes.
xlim = ax.get_xlim()
ylim = ax.get_ylim()
self.slc[self.axis] = slice(slider.value, slider.value + 1)
ax.clear()
# Need to use squeeze to collapse degenerate dimension (e.g. RGB image size 124 124 1 3)
ax.imshow(
np.squeeze(npa[tuple(self.slc)]),
cmap=plt.cm.Greys_r,
vmin=wl_slider.value[0],
vmax=wl_slider.value[1],
)
ax.set_title(title)
ax.set_axis_off()
# Set the zoom factor back to what it was before we cleared the axes, and rendered our data.
ax.set_xlim(xlim)
ax.set_ylim(ylim)
self.fig.canvas.draw_idle()
class ROIDataAquisition(object):
"""
This class provides a GUI for selecting box shaped Regions Of Interest (ROIs). Each ROI is represented as a
tuple: ((min_x,max_x),(min_y,max_y), and possibly (min_z,max_z)) if dealing with a 3D image.
When using the zoom/pan tool from the toolbar ROI selection is disabled. Once you click again on the zoom/pan
button zooming/panning will be disabled and ROI selection is enabled.
Note that when you are marking the ROI on a slice that is outside the Z-range selected by the
range slider, once you are done selecting the ROI, you will see no change on the current slice. This is the
correct behavior, though initially you may be surprised by it.
"""
def __init__(self, image, window_level=None, figure_size=(10, 8)):
self.image = image
(
self.npa,
self.min_intensity,
self.max_intensity,
) = self.get_window_level_numpy_array(self.image, window_level)
self.rois = []
# ROI display settings
self.roi_display_properties = dict(
facecolor="red", edgecolor="black", alpha=0.2, fill=True
)
ui = self.create_ui()
display(ui)
# Create a figure.
self.fig, self.axes = plt.subplots(1, 1, figsize=figure_size)
# Connect the mouse button press to the canvas (__call__ method is the invoked callback).
self.fig.canvas.mpl_connect("button_press_event", self)
self.roi_selector = RectangleSelector(
self.axes,
lambda eclick, erelease: None,
useblit=True,
button=[1, 3], # Left, right buttons only.
minspanx=5,
minspany=5, # Ignore motion smaller than 5 pixels.
spancoords="pixels",
interactive=True,
props=self.roi_display_properties,
)
self.roi_selector.set_visible(False)
# Display the data and the controls, first time we display the image is outside the "update_display" method
# as that method relies on the existence of a previous image which is removed from the figure.
self.axes.imshow(
self.npa[self.slice_slider.value, :, :] if self.slice_slider else self.npa,
cmap=plt.cm.Greys_r,
vmin=self.min_intensity,
vmax=self.max_intensity,
)
self.update_display()
def create_ui(self):
# Create the active UI components. Height and width are specified in 'em' units. This is
# a html size specification, size relative to current font size.
self.addroi_button = widgets.Button(
description="Add ROI", width="7em", height="3em"
)
self.addroi_button.on_click(self.add_roi)
self.clearlast_button = widgets.Button(
description="Clear Last", width="7em", height="3em"
)
self.clearlast_button.on_click(self.clear_last)
self.clearall_button = widgets.Button(
description="Clear All", width="7em", height="3em"
)
self.clearall_button.on_click(self.clear_all)
# Create sliders only if 3D image
self.slice_slider = self.roi_range_slider = None
if self.npa.ndim == 3:
self.roi_range_slider = widgets.IntRangeSlider(
description="ROI z range:",
min=0,
max=self.npa.shape[0] - 1,
step=1,
value=[0, self.npa.shape[0] - 1],
width="20em",
)
bx4 = widgets.Box(padding=15, children=[self.roi_range_slider])
self.slice_slider = widgets.IntSlider(
description="image z slice:",
min=0,
max=self.npa.shape[0] - 1,
step=1,
value=int((self.npa.shape[0] - 1) / 2),
width="20em",
)
self.slice_slider.observe(self.on_slice_slider_value_change, names="value")
bx0 = widgets.Box(padding=7, children=[self.slice_slider])
# Layout of UI components. This is pure ugliness because we are not using a UI toolkit. Layout is done
# using the box widget and padding so that the visible UI components are spaced nicely.
bx1 = widgets.Box(padding=7, children=[self.addroi_button])
bx2 = widgets.Box(padding=15, children=[self.clearlast_button])
bx3 = widgets.Box(padding=15, children=[self.clearall_button])
return (
widgets.HBox(
children=[
widgets.HBox(children=[bx1, bx2, bx3]),
widgets.VBox(children=[bx0, bx4]),
]
)
if self.npa.ndim == 3
else widgets.HBox(children=[widgets.HBox(children=[bx1, bx2, bx3])])
)
def on_slice_slider_value_change(self, change):
self.update_display()
def get_window_level_numpy_array(self, image, window_level):
npa = sitk.GetArrayViewFromImage(image)
# We don't take the minimum/maximum values, just in case there are outliers (top/bottom 2%)
if not window_level:
min_max = np.percentile(npa.flatten(), [2, 98])
return npa, min_max[0], min_max[1]
else:
return (
npa,
window_level[1] - window_level[0] / 2.0,
window_level[1] + window_level[0] / 2.0,
)
def update_display(self):
# Draw the image and ROIs.
# imshow adds an image to the axes, so we also remove the previous one.
self.axes.imshow(
self.npa[self.slice_slider.value, :, :] if self.slice_slider else self.npa,
cmap=plt.cm.Greys_r,
vmin=self.min_intensity,
vmax=self.max_intensity,
)
self.axes.images[0].remove()
# Iterate over all of the ROIs and only display/undisplay those that are relevant.
if self.slice_slider:
for roi_data in self.rois:
if (
self.slice_slider.value >= roi_data[3][0]
and self.slice_slider.value <= roi_data[3][1]
):
roi_data[0].set_visible(True)
else:
roi_data[0].set_visible(False)
self.axes.set_title(f"selected {len(self.rois)} ROIs")
self.axes.set_axis_off()
self.fig.canvas.draw_idle()
def add_roi_data(self, roi_data):
"""
Add regions of interest to this GUI.
Input is an iterable containing tuples where each tuple contains
either two or three tuples (min_x,max_x),(min_y,max_y), (min_z,max_z).
depending on the image dimensionality. The ROI
is the box defined by these integer values and includes
both min/max values.
"""
self.validate_rois(roi_data)
for roi in roi_data:
self.rois.append(
(
patches.Rectangle(
(roi[0][0], roi[1][0]),
roi[0][1] - roi[0][0],
roi[1][1] - roi[1][0],
**self.roi_display_properties,
),
roi[0],
roi[1],
roi[2] if self.npa.ndim == 3 else None,
)
)
self.axes.add_patch(self.rois[-1][0])
self.update_display()
def set_rois(self, roi_data):
"""
Clear any existing ROIs and set the display to the given ones.
Input is an iterable containing tuples where each tuple contains
two or three tuples (min_x,max_x),(min_y,max_y), (min_z,max_z) depending
on the image dimensionality. The ROI
is the box defined by these integer values and includes
both min/max values.
"""
self.clear_all_data()
self.add_roi_data(roi_data)
def validate_rois(self, roi_data):
for roi in roi_data:
for i, bounds in enumerate(roi, 1):
if bounds[0] > bounds[1]:
raise ValueError(
"First element in each tuple is expected to be smaller than second element, error in ROI ("
+ ", ".join(map(str, roi))
+ ")."
)
# Note that SimpleITK uses x-y-z specification vs. numpy's z-y-x
if not (