def __init__(self, center, radius, image_array, start_angle=0, ccw=True, sampling_ratio=1.0):
"""
Parameters
----------
image_array : ndarray
The 2D image array.
start_angle : int, float
Starting position of the profile in radians; 0 is right (0 on unit circle).
ccw : bool
If True (default), the profile will proceed counter-clockwise (the direction on the unit circle).
If False, will proceed clockwise.
sampling_ratio : float
The ratio of pixel sampling to real pixels. E.g. if 1.0, the profile will have approximately
the same number of elements as was encountered in the profile. A value of 2.0 will sample
the profile at 2x the number of elements.
See Also
--------
`~pylinac.core.geometry.Circle` : Further parameter info.
"""
Circle.__init__(self, center, radius)
self._ensure_array_size(image_array, self.radius + self.center.x, self.radius + self.center.y)
self.image_array = image_array
self.start_angle = start_angle
self.ccw = ccw
self.sampling_ratio = sampling_ratio
self._x_locations = None
self._y_locations = None
MultiProfile.__init__(self, self._profile)
def __init__(self, name, slice_array, angle, radius=None, dist_from_center=None):
"""
Parameters
----------
angle : int, float
The angle of the ROI in degrees from the phantom center.
.. warning::
Be sure the enter the angle in degrees rather than radians!
radius : int, float
The radius of the ROI from the center of the phantom.
dist_from_center : int, float
The distance of the ROI from the phantom center.
"""
ROI.__init__(self, name, slice_array)
Circle.__init__(self, radius=radius)
self.angle = angle
self.dist_from_center = dist_from_center
def __init__(self,
center,
radius,
image_array,
start_angle=0,
ccw=True,
sampling_ratio=1.0):
"""
Parameters
----------
image_array : ndarray
The 2D image array.
start_angle : int, float
Starting position of the profile in radians; 0 is right (0 on unit circle).
ccw : bool
If True (default), the profile will proceed counter-clockwise (the direction on the unit circle).
If False, will proceed clockwise.
sampling_ratio : float
The ratio of pixel sampling to real pixels. E.g. if 1.0, the profile will have approximately
the same number of elements as was encountered in the profile. A value of 2.0 will sample
the profile at 2x the number of elements.
See Also
--------
`~pylinac.core.geometry.Circle` : Further parameter info.
"""
Circle.__init__(self, center, radius)
self._ensure_array_size(image_array, self.radius + self.center.x,
self.radius + self.center.y)
self.image_array = image_array
self.start_angle = start_angle
self.ccw = ccw
self.sampling_ratio = sampling_ratio
self._x_locations = None
self._y_locations = None
MultiProfile.__init__(self, self._profile)
def _create_phantom_outline_object(self):
"""Construct the phantom outline object which will be plotted on the image for visual inspection."""
outline_type = list(self.phantom_outline_object)[0]
outline_settings = list(self.phantom_outline_object.values())[0]
settings = {}
if outline_type == 'Rectangle':
side_a = self.phantom_radius*outline_settings['width ratio']
side_b = self.phantom_radius*outline_settings['height ratio']
half_hyp = np.sqrt(side_a**2 + side_b**2)/2
internal_angle = ia = np.rad2deg(np.arctan(side_b/side_a))
new_x = self.phantom_center.x + half_hyp*(geometry.cos(ia)-geometry.cos(ia+self.phantom_angle))
new_y = self.phantom_center.y + half_hyp*(geometry.sin(ia)-geometry.sin(ia+self.phantom_angle))
obj = Rectangle(width=self.phantom_radius*outline_settings['width ratio'],
height=self.phantom_radius*outline_settings['height ratio'],
center=Point(new_x, new_y))
settings['angle'] = self.phantom_angle
elif outline_type == 'Circle':
obj = Circle(center_point=self.phantom_center,
radius=self.phantom_radius*outline_settings['radius ratio'])
else:
raise ValueError("An outline object was passed but was not a Circle or Rectangle.")
return obj, settings
def plot_analyzed_image(self,
image=True,
low_contrast=True,
high_contrast=True,
show=True):
"""Plot the analyzed image, which includes the original image with ROIs marked, low-contrast plots
and high-contrast plots.
Parameters
----------
image : bool
Show the image.
low_contrast : bool
Show the low contrast values plot.
high_contrast : bool
Show the high contrast values plot.
show : bool
Whether to actually show the image when called.
"""
num_plots = sum((image, low_contrast, high_contrast))
if num_plots < 1:
return
fig, axes = plt.subplots(1, num_plots)
fig.subplots_adjust(wspace=0.4)
if num_plots < 2:
axes = (axes, )
axes = iter(axes)
if image:
img_ax = next(axes)
self.image.plot(ax=img_ax, show=False)
img_ax.axis('off')
img_ax.set_title('Leeds TOR Phantom Analysis')
# plot the phantom location circle; helps users identify if the phantom location was identified correctly
Circle(center_point=self.phantom_center,
radius=self.phantom_radius).plot2axes(img_ax,
edgecolor='blue')
# plot the low contrast ROIs
for roi in self.lc_rois:
roi.plot2axes(img_ax, edgecolor=roi.plot_color_constant)
for roi in self.lc_ref_rois:
roi.plot2axes(img_ax, edgecolor='g')
# plot the high-contrast ROIs
for roi in self.hc_rois:
roi.plot2axes(img_ax, edgecolor=roi.plot_color)
for roi in self.hc_ref_rois:
roi.plot2axes(img_ax, edgecolor='g')
# plot the low contrast values
if low_contrast:
lowcon_ax = next(axes)
self._plot_lowcontrast(lowcon_ax, self.lc_rois,
self.low_contrast_threshold)
# plot the high contrast MTF
if high_contrast:
hicon_ax = next(axes)
hc_rois = [roi.mtf for roi in self.hc_rois]
hc_rois.insert(0, 1)
self._plot_highcontrast(hicon_ax, hc_rois,
self.hi_contrast_threshold)
if show:
plt.show()
def __init__(self, center=None, radius=None):
Circle.__init__(self, center, radius)
Profile.__init__(self)
self.x_locs = np.ndarray # x-values of the circle profile's location
self.y_locs = np.ndarray # y-values of the circle profile's location
def distance(p, things):
"""Calculate the maximum distance to any line from the given point."""
other_thing = Point(p[0], p[1], p[2])
if axis == COUCH:
other_thing = Circle(other_thing, radius=p[3])
return max(thing.distance_to(other_thing) for thing in things)
