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 main():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR + '/visualization')
log_dir = 'log_fisher_grid5_nonlinear'
#Load gaussians
# gmm_filename = os.path.join(log_dir,'gmm.p')
# gmm = pickle.load(open(gmm_filename, "rb"))
# parameters_filename = os.path.join(log_dir,'parameters.p')
# PARAMETERS = pickle.load(open(parameters_filename, "rb"))
#Create new gaussian
subdev = 10
variance = 0.01
gmm = utils.get_grid_gmm(subdivisions=[subdev, subdev, subdev],
variance=variance)
class helper_struct():
def __init__(self):
self.num_gaussians = subdev
self.gmm_type = 'grid'
PARAMETERS = helper_struct()
gaussian_index = 740
num_points = 1024
model_idx = 0
n_gaussians = np.power(
PARAMETERS.num_gaussians,
3) if PARAMETERS.gmm_type == 'grid' else PARAMETERS.num_gaussians
points, _ = provider.load_single_model(model_idx=model_idx,
test_train='train',
file_idxs=0,
num_points=num_points)
g_pts, g_probs = utils.get_gaussian_points(points,
gmm,
idx=gaussian_index,
thresh=0.01)
#draw_gaussian_points(points, g_pts, gmm, idx=gaussian_index, ax=None, display=True, color_val=g_probs)
#fv = utils.fisher_vector(points, gmm, normalization=True)
# d_pi = fv[0:n_gaussians]
# mean_d_pi = 0.02
# ax=draw_point_cloud(points)
# draw_gaussians(gmm, ax=ax, display=True, mappables=d_pi, thresh=mean_d_pi)
per_point_dpi, per_point_d_mu, per_point_d_sigma = utils.fisher_vector_per_point(
points, gmm)
visualize_derivatives(points, gmm, gaussian_index, per_point_dpi,
per_point_d_mu, per_point_d_sigma)
e = (2 * beta[:, 2] / norm_N0).view(-1, 1, 1)
f = (beta[:, 4] / norm_N0).view(-1, 1, 1)
g = (2 * beta[:, 3] / norm_N0).view(-1, 1, 1)
II = torch.cat(
[torch.cat([e, f], dim=2),
torch.cat([f, g], dim=2)], dim=1)
M_weingarten = -torch.bmm(torch.inverse(I), II)
curvatures, dirs = torch.symeig(M_weingarten,
eigenvectors=True) #faster
dirs = torch.cat(
[dirs,
torch.zeros(dirs.shape[0], 2, 1, device=dirs.device)],
dim=2) # pad zero in the normal direction
return curvatures, dirs
if __name__ == "__main__":
model_idx = 0
num_points = 1024
gmm = get_3d_grid_gmm(subdivisions=[5, 5, 5], variance=0.04)
points = provider.load_single_model(model_idx=model_idx,
train_file_idxs=0,
num_points=num_points)
points = np.tile(np.expand_dims(points, 0), [128, 1, 1])
fv_gpu = get_fisher_vectors(points, gmm, normalization=True)