def visualize_pc(points,
label_title=None,
fig_title='figure',
export=False,
filename='fv_pc',
display=False):
""" visualizes the point cloud representation as an image
INPUT:
OUTPUT:
"""
f = plt.figure()
ax = plt.axes()
f.canvas.set_window_title(fig_title)
# plt.get_current_fig_manager().window.wm_geometry(str(pos[0]) + "x" + str(pos[128]) + "+"+str(pos[256])+"+"+str(pos[512]))
image = pc_util.point_cloud_isoview(points[0, :, :])
image = np.ma.masked_where(image < 0.0005, image)
cmap = plt.cm.rainbow
cmap.set_bad(color='white')
ax.imshow(image, cmap=cmap)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.set_title(label_title)
#ax.axis('off')
if export:
plt.savefig(filename + '.pdf',
format='pdf',
bbox_inches='tight',
dpi=1000)
if display:
plt.show()
def visualize_single_fv_with_pc(fv, points, label_title=None, fig_title='figure', type='minmax', pos=[750,800,0,0], export=False, filename='fv_pc'):
""" visualizes the fisher vector representation as an image
INPUT: fv - B X n_gaussians X n_components - fisher vector representation
points B X n_points X 64
OUTPUT: None (opens a window and draws the axes)
"""
n_models = fv.shape[0]
cmap = "seismic"
scalefactor = 1
vmin = -1 * scalefactor
vmax = 1 * scalefactor
if type == 'generic':
derivatives = ["d_pi", "d_mu1", "d_mu2", "d_mu3", "d_sig1", "d_sig2", "d_sig3"]
elif type == 'minmax':
derivatives = ["d_pi_max","d_pi_sum",
"d_mu1_max", "d_mu2_max", "d_mu3_max",
"d_mu1_min", "d_mu2_min", "d_mu3_min",
"d_mu1_sum", "d_mu2_sum", "d_mu3_sum",
"d_sig1_max", "d_sig2_max", "d_sig3_max",
"d_sig1_min", "d_sig2_min", "d_sig3_min",
"d_sig1_sum", "d_sig2_sum", "d_sig3_sum"]
else:
derivatives = []
tick_marks = np.arange(len(derivatives))
f = plt.figure()
f.canvas.set_window_title(fig_title)
ax1 = plt.axes([0.05, 0.5, 0.45, 0.2])
ax2 = plt.axes([0.5, 0.5, 0.3, 0.3])
#plt.get_current_fig_manager().window.wm_geometry(str(pos[0]) + "x" + str(pos[128]) + "+"+str(pos[256])+"+"+str(pos[512]))
#for i in range(n_models):
ax1.imshow(fv[0,:,:], cmap=cmap, vmin=vmin, vmax=vmax)
ax1.set_title(label_title)
ax1.set_xticks([])
ax1.set_yticks([])
# ax[i, 0].axis('off')
ax2.set_yticks(tick_marks)
ax2.set_yticklabels(derivatives)
ax2.tick_params(labelsize=3)
#image = pc_util.point_cloud_three_views(points[i, :, :])
image = pc_util.point_cloud_isoview(points[0,:,:])
image = np.ma.masked_where(image < 0.0005, image)
cmap = plt.cm.rainbow
cmap.set_bad(color='white')
ax2.imshow(image, cmap=cmap)
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(False)
#fig.patch.set_visible(False)
ax2.axis('off')
if export:
plt.savefig(filename + '.pdf', format='pdf', bbox_inches='tight', dpi=1000)
