def update_section_based_data(self, mask, shape, axis, delta,
islice, coor, obj):
"""
Estimate volume and surface of object classes from their sections.
WARNING: Surface estimation is very crude!
Parameters
----------
mask : bool array
Slice mask, True where the object inside is.
shape : tuple
Real 2D shape of the mask.
axis : 'x', 'y' or 'z'
Axis perpendicular to the slices.
delta : float
Slice distance.
islice : int
Index of the section.
coor : float
Coordinate of the section along the axis.
obj : str
Geometrical object.
"""
obj_class = obj.obj_class
pixel_area = self.box.get_pixel_area(axis)
volumes = self.section_volumes[axis]
volumes[obj_class] += pixel_area * np.sum(mask) * delta
mask.shape = shape
pixel_sizes = self.box.get_pixel_sizes(axis)
grad0, grad1 = np.gradient(mask)
val0 = len(grad0[np.where(grad0)]) * pixel_sizes[0]
val1 = len(grad1[np.where(grad1)]) * pixel_sizes[1]
circumference = 0.5 * (val0 + val1)
self.section_circumferences[obj.name].append((axis, islice, coor,
circumference))
surfaces = self.section_surfaces[axis]
surfaces[obj_class] += circumference * delta
def contains(self, points):
"""
Point x in ellipsoid A <=> x^T A x <= 1.
Works for array of points.
Parameters:
points : (n_point, 3) array
"""
points = np.array(points, ndmin=2, dtype=np.float64)
x = points.T - self.centre[:, np.newaxis]
aux = np.sum(x * np.dot(self.mtx, x), axis=0)
mask = np.where(aux <= 1.0, True, False)
## x2 = np.r_[points.T, np.ones((1,points.shape[0]))]
## aux2 = np.sum(x2 * np.dot(self.mtx_hc, x2), axis = 0)
## mask2 = np.where(aux2 <= 0.0, True, False)
## print np.alltrue(mask == mask2)
return mask
def contains(self, points):
"""
Point x in ellipsoid A <=> x^T A x <= 1.
Works for array of points.
Parameters:
points : (n_point, 3) array
"""
points = np.array(points, ndmin=2, dtype=np.float64)
x = points.T - self.centre[:,np.newaxis]
aux = np.sum(x * np.dot(self.mtx, x), axis = 0)
mask = np.where(aux <= 1.0, True, False)
## x2 = np.r_[points.T, np.ones((1,points.shape[0]))]
## aux2 = np.sum(x2 * np.dot(self.mtx_hc, x2), axis = 0)
## mask2 = np.where(aux2 <= 0.0, True, False)
## print np.alltrue(mask == mask2)
return mask