Added visualization for motion capture data using python visual module.

GPy/util/visualize.py

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