def visualize_pc_with_svd():
num_points = 1024
model_idx = 5
gpu_idx = 0
original_points, _ = provider.load_single_model(model_idx=model_idx,
test_train='train',
file_idxs=0,
num_points=num_points)
original_points = provider.rotate_point_cloud_by_angle(
original_points, np.pi / 2)
# #Simple plane sanity check
# original_points = np.concatenate([np.random.rand(2, 1024), np.zeros([1, 1024])],axis=0)
# R = np.array([[0.7071, 0, 0.7071],
# [0, 1, 0],
# [-0.7071, 0, 0.7071]])
# original_points = np.transpose(np.dot(R ,original_points))
original_points = np.expand_dims(original_points, 0)
pc_util.pyplot_draw_point_cloud(original_points[0, :, :])
sess = tf_util.get_session(gpu_idx, limit_gpu=True)
points_pl = tf.placeholder(tf.float32, shape=(1, num_points, 3))
svd_op = tf_util.pc_svd(points_pl)
rotated_points = sess.run(svd_op, feed_dict={points_pl: original_points})
pc_util.pyplot_draw_point_cloud(rotated_points[0, :, :])
plt.show()
def plot_advsarial_samples(self, pointclouds_pl_adv, labels_pl, pred_val):
for i in range(labels_pl.shape[0]):
if labels_pl[i]!=pred_val[i] and not self.count[labels_pl[i]]:
img_filename = 'label_%s_pred_%s.jpg' % (SHAPE_NAMES[labels_pl[i]],
SHAPE_NAMES[pred_val[i]])
img_filename = os.path.join(DUMP_DIR, img_filename)
pc_util.pyplot_draw_point_cloud(pointclouds_pl_adv[i], img_filename)
self.count[labels_pl[i]] = True