def plot_point_cloud(disp_arr: np.ndarray,
rgb_img: np.ndarray = None,
down_scale: int = 1,
view_angles: (int, int) = (10, 135),
s: float = 0.5,
ax: Axes3D = None,
show_axes: bool = False) -> Axes3D:
"""
Plots a point cloud using the famous matplotlib.
:param disp_arr: numpy array [MxN], representing the disparity map
:param rgb_img: numpy array [MxNx3], containing RGB pixel colors
:param down_scale: int, downscale factor
:param view_angles: tuple(int, int) containing elevator and azimuth angle, respectively
:param s: float, size of a point
:param ax: Axes3D object, optional for accumulative plotting
:param show_axes: bool, option for axes plot
:return: Axes3D object, containing point cloud
"""
# validate downscale value
if down_scale < 1 or down_scale > min(disp_arr.shape[:2]) // 2:
raise IndexError('Downscale factor is %s and out-of-range.' %
down_scale)
# rgb image presence/absence handling
if rgb_img is None or disp_arr.shape[:2] != rgb_img.shape[:2] or len(
rgb_img.shape) != 3:
rgb = np.zeros(disp_arr.shape + (3, ))[::down_scale, ::down_scale, ...]
if rgb_img is not None:
warnings.warn('Depth map and RGB image dimension mismatch.')
else:
# flip x-axis and downscale rgb image
rgb = rgb_img[:, ::-1, ...][::down_scale, ::down_scale, ...]
# normalize rgb values to 0-1 range
rgb = Normalizer(rgb).type_norm(new_min=max(0, rgb.min()), new_max=1)
# flip x-axis and downscale depth map
zz = disp_arr[:, ::-1][::down_scale, ::down_scale, ...]
xx, yy = np.meshgrid(np.arange(zz.shape[1]), np.arange(zz.shape[0]))
# sort according to depth value to avoid occlusion order problem
order = np.argsort(zz.ravel())
# plot depth data
fig, ax = (plt.figure(),
plt.axes(projection='3d')) if ax is None else (None, ax)
ax.set_axis_on() if show_axes else ax.set_axis_off()
ax.scatter(xx.ravel()[order],
yy.ravel()[order],
zz.ravel()[order],
c=rgb.reshape(-1, rgb.shape[2])[order],
s=s)
ax.view_init(view_angles[0], view_angles[1])
ax.set_ylim(0, zz.shape[0])
ax.set_xlim(0, zz.shape[1])
return ax
def scatter(self, *args, **kwargs):
'''
If the **mantid3d** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes3D.scatter` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})
ax.scatter(workspace) #for workspaces
ax.scatter(x,y,z) #for arrays
fig.show()
For keywords related to workspaces, see :func:`mantid.plots.plotfunctions3D.scatter`
'''
if mantid.plots.helperfunctions.validate_args(*args):
mantid.kernel.logger.debug('using mantid.plots.plotfunctions3D')
return mantid.plots.plotfunctions3D.scatter(self, *args, **kwargs)
else:
return Axes3D.scatter(self, *args, **kwargs)
def scatter(self, *args, **kwargs):
'''
If the **mantid3d** projection is chosen, it can be
used the same as :py:meth:`matplotlib.axes.Axes3D.scatter` for arrays,
or it can be used to plot :class:`mantid.api.MatrixWorkspace`
or :class:`mantid.api.IMDHistoWorkspace`. You can have something like::
import matplotlib.pyplot as plt
from mantid import plots
...
fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})
ax.scatter(workspace) #for workspaces
ax.scatter(x,y,z) #for arrays
fig.show()
For keywords related to workspaces, see :func:`mantid.plots.plotfunctions3D.scatter`
'''
if mantid.plots.helperfunctions.validate_args(*args):
mantid.kernel.logger.debug('using mantid.plots.plotfunctions3D')
return mantid.plots.plotfunctions3D.scatter(self, *args, **kwargs)
else:
return Axes3D.scatter(self, *args, **kwargs)
