def predict_joints(model_id, args):
"""
predict joints for a specified model
:param model_id: processed model ID number
:param args:
:return: predicted joints, and voxelized mesh
"""
vox_folder = os.path.join(args.dataset_folder, 'vox/')
mesh_folder = os.path.join(args.dataset_folder, 'obj_remesh/')
raw_pred = os.path.join(args.res_folder, '{:d}.ply'.format(model_id))
vox_file = os.path.join(vox_folder, '{:d}.binvox'.format(model_id))
mesh_file = os.path.join(mesh_folder, '{:d}.obj'.format(model_id))
pred_attn = np.load(
os.path.join(args.res_folder, '{:d}_attn.npy'.format(model_id)))
with open(vox_file, 'rb') as fvox:
vox = binvox_rw.read_as_3d_array(fvox)
pred_joints = readPly(raw_pred)
pred_joints, index_inside = inside_check(pred_joints, vox)
pred_attn = pred_attn[index_inside, :]
# img = draw_shifted_pts(mesh_file, pred_joints, weights=pred_attn)
bandwidth = np.load(
os.path.join(args.res_folder, '{:d}_bandwidth.npy'.format(model_id)))
bandwidth = bandwidth[0]
pred_joints = pred_joints[pred_attn.squeeze() > 1e-3]
pred_attn = pred_attn[pred_attn.squeeze() > 1e-3]
# reflect raw points
pred_joints_reflect = pred_joints * np.array([[-1, 1, 1]])
pred_joints = np.concatenate((pred_joints, pred_joints_reflect), axis=0)
pred_attn = np.tile(pred_attn, (2, 1))
# img = draw_shifted_pts(mesh_file, pred_joints, weights=pred_attn)
# cv2.imwrite(os.path.join(res_folder, '{:s}_raw.jpg'.format(model_id)), img[:, :, ::-1])
pred_joints = meanshift_cluster(pred_joints,
bandwidth,
pred_attn,
max_iter=20)
Y_dist = np.sum(
((pred_joints[np.newaxis, ...] - pred_joints[:, np.newaxis, :])**2),
axis=2)
density = np.maximum(bandwidth**2 - Y_dist, np.zeros(Y_dist.shape))
# density = density * pred_attn
density = np.sum(density, axis=0)
density_sum = np.sum(density)
pred_joints_ = pred_joints[density / density_sum > args.threshold_best]
density_ = density[density / density_sum > args.threshold_best]
pred_joints_ = nms_meanshift(pred_joints_, density_, bandwidth)
pred_joints_, _ = flip(pred_joints_)
reduce_threshold = args.threshold_best
while len(pred_joints_) < 2 and reduce_threshold > 1e-7:
# print('reducing')
reduce_threshold = reduce_threshold / 1.3
pred_joints_ = pred_joints[density / density_sum >= reduce_threshold]
density_ = density[density / density_sum > reduce_threshold]
pred_joints_ = nms_meanshift(pred_joints_, density_, bandwidth)
pred_joints_, _ = flip(pred_joints_)
if reduce_threshold <= 1e-7:
pred_joints_ = nms_meanshift(pred_joints_, density, bandwidth)
pred_joints_, _ = flip(pred_joints_)
pred_joints = pred_joints_
# img = draw_shifted_pts(mesh_file, pred_joints)
# cv2.imwrite(os.path.join(res_folder, '{:d}_joint.jpg'.format(model_id)), img)
# np.save(os.path.join(res_folder, '{:d}_joint.npy'.format(model_id)), pred_joints)
return pred_joints, vox
def predict_joints(input_data,
vox,
joint_pred_net,
threshold,
bandwidth=None,
mesh_filename=None):
"""
Predict joints
:param input_data: wrapped input data
:param vox: voxelized mesh
:param joint_pred_net: network for predicting joints
:param threshold: density threshold to filter out shifted points
:param bandwidth: bandwidth for meanshift clustering
:param mesh_filename: mesh filename for visualization
:return: wrapped data with predicted joints, pair-wise bone representation added.
"""
data_displacement, _, attn_pred, bandwidth_pred = joint_pred_net(
input_data)
y_pred = data_displacement + input_data.pos
y_pred_np = y_pred.data.cpu().numpy()
attn_pred_np = attn_pred.data.cpu().numpy()
y_pred_np, index_inside = inside_check(y_pred_np, vox)
attn_pred_np = attn_pred_np[index_inside, :]
y_pred_np = y_pred_np[attn_pred_np.squeeze() > 1e-3]
attn_pred_np = attn_pred_np[attn_pred_np.squeeze() > 1e-3]
# symmetrize points by reflecting
y_pred_np_reflect = y_pred_np * np.array([[-1, 1, 1]])
y_pred_np = np.concatenate((y_pred_np, y_pred_np_reflect), axis=0)
attn_pred_np = np.tile(attn_pred_np, (2, 1))
#img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
if bandwidth is None:
bandwidth = bandwidth_pred.item()
y_pred_np = meanshift_cluster(y_pred_np,
bandwidth,
attn_pred_np,
max_iter=40)
#img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
Y_dist = np.sum(
((y_pred_np[np.newaxis, ...] - y_pred_np[:, np.newaxis, :])**2),
axis=2)
density = np.maximum(bandwidth**2 - Y_dist, np.zeros(Y_dist.shape))
density = np.sum(density, axis=0)
density_sum = np.sum(density)
y_pred_np = y_pred_np[density / density_sum > threshold]
attn_pred_np = attn_pred_np[density / density_sum > threshold][:, 0]
density = density[density / density_sum > threshold]
#img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
pred_joints = nms_meanshift(y_pred_np, density, bandwidth)
pred_joints, _ = flip(pred_joints)
#img = draw_shifted_pts(mesh_filename, pred_joints)
# prepare and add new data members
pairs = list(it.combinations(range(pred_joints.shape[0]), 2))
pair_attr = []
for pr in pairs:
dist = np.linalg.norm(pred_joints[pr[0]] - pred_joints[pr[1]])
bone_samples = sample_on_bone(pred_joints[pr[0]], pred_joints[pr[1]])
bone_samples_inside, _ = inside_check(bone_samples, vox)
outside_proportion = len(bone_samples_inside) / (len(bone_samples) +
1e-10)
attr = np.array([dist, outside_proportion, 1])
pair_attr.append(attr)
pairs = np.array(pairs)
pair_attr = np.array(pair_attr)
pairs = torch.from_numpy(pairs).float()
pair_attr = torch.from_numpy(pair_attr).float()
pred_joints = torch.from_numpy(pred_joints).float()
joints_batch = torch.zeros(len(pred_joints), dtype=torch.long)
pairs_batch = torch.zeros(len(pairs), dtype=torch.long)
input_data.joints = pred_joints
input_data.pairs = pairs
input_data.pair_attr = pair_attr
input_data.joints_batch = joints_batch
input_data.pairs_batch = pairs_batch
return input_data
def predict_joints(input_data,
vox,
joint_pred_net,
threshold,
bandwidth=None,
mesh_filename=None):
"""
Predict joints
:param input_data: wrapped input data
:param vox: voxelized mesh
:param joint_pred_net: network for predicting joints
:param threshold: density threshold to filter out shifted points
:param bandwidth: bandwidth for meanshift clustering
:param mesh_filename: mesh filename for visualization
:return: wrapped data with predicted joints, pair-wise bone representation added.
"""
data_displacement, _, attn_pred, bandwidth_pred = joint_pred_net(
input_data)
y_pred = data_displacement + input_data.pos
y_pred_np = y_pred.data.cpu().numpy()
attn_pred_np = attn_pred.data.cpu().numpy()
y_pred_np, index_inside = inside_check(y_pred_np, vox)
attn_pred_np = attn_pred_np[index_inside, :]
y_pred_np = y_pred_np[attn_pred_np.squeeze() > 1e-3]
attn_pred_np = attn_pred_np[attn_pred_np.squeeze() > 1e-3]
# symmetrize points by reflecting
y_pred_np_reflect = y_pred_np * np.array([[-1, 1, 1]])
y_pred_np = np.concatenate((y_pred_np, y_pred_np_reflect), axis=0)
attn_pred_np = np.tile(attn_pred_np, (2, 1))
#img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
if bandwidth is None:
bandwidth = bandwidth_pred
y_pred_np = meanshift_cluster(y_pred_np,
bandwidth,
attn_pred_np,
max_iter=40)
#img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
Y_dist = np.sum(
((y_pred_np[np.newaxis, ...] - y_pred_np[:, np.newaxis, :])**2),
axis=2)
density = np.maximum(bandwidth**2 - Y_dist, np.zeros(Y_dist.shape))
density = np.sum(density, axis=0)
density_sum = np.sum(density)
y_pred_np = y_pred_np[density / density_sum > threshold]
attn_pred_np = attn_pred_np[density / density_sum > threshold][:, 0]
density = density[density / density_sum > threshold]
#img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
pred_joints = nms_meanshift(y_pred_np, density, bandwidth)
pred_joints, _ = flip(pred_joints)
#img = draw_shifted_pts(mesh_filename, pred_joints)
# prepare and add new data members
num_joint = len(pred_joints)
pair_all = []
for joint1_id in range(len(pred_joints)):
for joint2_id in range(joint1_id + 1, len(pred_joints)):
dist = np.linalg.norm(pred_joints[joint1_id] -
pred_joints[joint2_id])
bone_samples = sample_on_bone(pred_joints[joint1_id],
pred_joints[joint2_id])
bone_samples_inside, _ = inside_check(bone_samples, vox)
outside_proportion = len(bone_samples_inside) / (
len(bone_samples) + 1e-10)
pair = np.array(
[joint1_id, joint2_id, dist, outside_proportion, 1])
pair_all.append(pair)
pair_all = np.array(pair_all)
num_pair = len(pair_all)
pair_all = torch.from_numpy(pair_all).float()
if num_joint < len(input_data.pos):
pred_joints = np.tile(
pred_joints,
(round(1.0 * len(input_data.pos) / num_joint + 0.5), 1))
pred_joints = pred_joints[:len(input_data.pos), :]
pred_joints = torch.from_numpy(pred_joints).float()
input_data.y = pred_joints
input_data.num_joint = [num_joint]
input_data.pairs = pair_all
input_data.num_pair = [num_pair]
return input_data
