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Added visualization for motion capture data using python visual module.
This commit is contained in:
Neil Lawrence
parent
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commit
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1 changed files with 87 additions and 20 deletions
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@ -5,6 +5,7 @@ import numpy as np
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import matplotlib as mpl
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import time
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import Image
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import visual
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class data_show:
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"""
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@ -13,26 +14,35 @@ class data_show:
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stick figure, or images are shown using imshow. This class enables latent
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to data visualizations for the GP-LVM.
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"""
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def __init__(self, vals, axes=None):
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def __init__(self, vals):
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self.vals = vals.copy()
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# If no axes are defined, create some.
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def modify(self, vals):
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raise NotImplementedError, "this needs to be implemented to use the data_show class"
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class matplotlib_show(data_show):
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"""
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the matplotlib_show class is a base class for all visualization methods that use matplotlib. It is initialized with an axis. If the axis is set to None it creates a figure window.
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"""
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def __init__(self, vals, axes=None):
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data_show.__init__(self, vals)
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# If no axes are defined, create some.
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if axes==None:
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fig = plt.figure()
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self.axes = fig.add_subplot(111)
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else:
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self.axes = axes
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def modify(self, vals):
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raise NotImplementedError, "this needs to be implemented to use the data_show class"
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class vector_show(data_show):
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class vector_show(matplotlib_show):
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"""
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A base visualization class that just shows a data vector as a plot of
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vector elements alongside their indices.
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"""
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def __init__(self, vals, axes=None):
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data_show.__init__(self, vals, axes)
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matplotlib_show.__init__(self, vals, axes)
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self.handle = self.axes.plot(np.arange(0, len(vals))[:, None], self.vals.T)[0]
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def modify(self, vals):
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@ -42,7 +52,7 @@ class vector_show(data_show):
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self.axes.figure.canvas.draw()
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class lvm(data_show):
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class lvm(matplotlib_show):
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def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0,1]):
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"""Visualize a latent variable model
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@ -54,7 +64,7 @@ class lvm(data_show):
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if vals == None:
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vals = model.X[0]
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data_show.__init__(self, vals, axes=latent_axes)
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matplotlib_show.__init__(self, vals, axes=latent_axes)
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if isinstance(latent_axes,mpl.axes.Axes):
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self.cid = latent_axes.figure.canvas.mpl_connect('button_press_event', self.on_click)
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@ -204,10 +214,10 @@ class lvm_dimselect(lvm):
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class image_show(data_show):
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class image_show(matplotlib_show):
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"""Show a data vector as an image."""
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def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False, palette=[], presetMean = 0., presetSTD = -1., selectImage=0):
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data_show.__init__(self, vals, axes)
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matplotlib_show.__init__(self, vals, axes)
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self.dimensions = dimensions
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self.transpose = transpose
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self.invert = invert
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@ -266,14 +276,72 @@ class image_show(data_show):
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self.vals = Image.fromarray(self.vals.astype('uint8'))
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self.vals.putpalette(self.palette) # palette is a list, must be loaded before calling this function
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class mocap_data_show(data_show):
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class mocap_data_show_visual(data_show):
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"""Base class for visualizing motion capture data using visual module."""
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def __init__(self, vals, connect=None, radius=0.1):
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data_show.__init__(self, vals)
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self.radius = radius
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self.connect = connect
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self.process_values()
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self.draw_edges()
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self.draw_vertices()
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def draw_vertices(self):
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self.spheres = []
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for i in range(self.vals.shape[0]):
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self.spheres.append(visual.sphere(pos=(self.vals[i, 0], self.vals[i, 2], self.vals[i, 1]), radius=self.radius))
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def draw_edges(self):
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self.rods = []
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self.line_handle = []
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if not self.connect==None:
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self.I, self.J = np.nonzero(self.connect)
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for i, j in zip(self.I, self.J):
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pos, axis = self.pos_axis(i, j)
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self.rods.append(visual.cylinder(pos=pos, axis=axis, radius=self.radius))
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def modify_vertices(self):
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for i in range(self.vals.shape[0]):
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self.spheres[i].pos = (self.vals[i, 0], self.vals[i, 2], self.vals[i, 1])
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def modify_edges(self):
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self.line_handle = []
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if not self.connect==None:
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self.I, self.J = np.nonzero(self.connect)
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for rod, i, j in zip(self.rods, self.I, self.J):
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rod.pos, rod.axis = self.pos_axis(i, j)
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def pos_axis(self, i, j):
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pos = []
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axis = []
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pos.append(self.vals[i, 0])
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axis.append(self.vals[j, 0]-self.vals[i,0])
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pos.append(self.vals[i, 2])
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axis.append(self.vals[j, 2]-self.vals[i,2])
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pos.append(self.vals[i, 1])
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axis.append(self.vals[j, 1]-self.vals[i,1])
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return pos, axis
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def modify(self, vals):
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self.vals = vals.copy()
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self.process_values()
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self.modify_edges()
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self.modify_vertices()
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def process_values(self):
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raise NotImplementedError, "this needs to be implemented to use the data_show class"
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class mocap_data_show(matplotlib_show):
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"""Base class for visualizing motion capture data."""
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def __init__(self, vals, axes=None, connect=None):
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if axes==None:
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fig = plt.figure()
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axes = fig.add_subplot(111, projection='3d')
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data_show.__init__(self, vals, axes)
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matplotlib_show.__init__(self, vals, axes)
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self.connect = connect
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self.process_values()
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@ -342,17 +410,17 @@ class mocap_data_show(data_show):
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self.axes.set_zlim(self.z_lim)
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class stick_show(mocap_data_show):
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class stick_show(mocap_data_show_visual):
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"""Show a three dimensional point cloud as a figure. Connect elements of the figure together using the matrix connect."""
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def __init__(self, vals, axes=None, connect=None):
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mocap_data_show.__init__(self, vals, axes, connect)
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def __init__(self, vals, connect=None):
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mocap_data_show_visual.__init__(self, vals, connect, radius=0.04)
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def process_values(self):
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self.vals = self.vals.reshape((3, self.vals.shape[1]/3)).T
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class skeleton_show(mocap_data_show):
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class skeleton_show(mocap_data_show_visual):
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"""data_show class for visualizing motion capture data encoded as a skeleton with angles."""
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def __init__(self, vals, skel, padding=0, axes=None):
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def __init__(self, vals, skel, padding=0):
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"""data_show class for visualizing motion capture data encoded as a skeleton with angles.
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:param vals: set of modeled angles to use for printing in the axis when it's first created.
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:type vals: np.array
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@ -364,8 +432,7 @@ class skeleton_show(mocap_data_show):
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self.skel = skel
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self.padding = padding
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connect = skel.connection_matrix()
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mocap_data_show.__init__(self, vals, axes, connect)
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mocap_data_show_visual.__init__(self, vals, connect, radius=0.4)
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def process_values(self):
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"""Takes a set of angles and converts them to the x,y,z coordinates in the internal prepresentation of the class, ready for plotting.
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