def test_gpu(gpu_id=[0]):
if len(gpu_id) > 0 and torch.cuda.is_available():
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id[0])
device = torch.device('cuda')
else:
device = torch.device('cpu')
print(device)
pose_size = 72
beta_size = 10
np.random.seed(9608)
pose = torch.from_numpy(get_theta('00001'))\
.type(torch.float64).to(device)
betas = torch.from_numpy(get_beta('00001')) \
.type(torch.float64).to(device)
trans = torch.from_numpy(get_trans('00001')).type(torch.float64).to(device)
outmesh_path = './smpl_torch.obj'
model = SMPLModel(device=device)
result = model(betas, pose, trans)
verts_numpy = result.cpu().numpy()
faces_numpy = model.faces
np.save('../../resault/verts.npy', verts_numpy)
np.save('../../resault/faces.npy', faces_numpy)
def smplTest():
smpl = smpl_torch.SMPLModel(device)
pose = torch.from_numpy(get_theta('00000')).type(torch.float64).to(device)
beta = torch.from_numpy(get_beta('00000')) \
.type(torch.float64).to(device)
trans = torch.from_numpy(get_trans('00000')).type(torch.float64).to(device)
res = smpl(beta, pose, trans)
J = smpl.J_regressor
J = J.mm(res)
print(J)
def get_ellipse(x, y): a = np.column_stack((x, y)) a = a - np.repeat(a.mean(axis=0), a.shape[0]).reshape(a.shape[1], a.shape[0]).T # mean center (w, v) = np.linalg.eig(np.cov(a.T)) i = np.nonzero((-w).argsort()==0)[0][0] # indices for the top 2 eigenvalues j = np.nonzero((-w).argsort()==1)[0][0] xy = (np.mean(x), np.mean(y)) width = np.sqrt(w[i]) * 2 height = np.sqrt(w[j]) * 2 unit = np.array([1, 0]) vi = v[:,i] #vi = vi / norm(vi) # already normalized theta = utils.get_theta(vi, unit) angle = utils.rad_to_deg(theta) return (xy, width, height, angle)
def get_ellipse(x, y):
a = np.column_stack((x, y))
a = a - np.repeat(a.mean(axis=0), a.shape[0]).reshape(
a.shape[1], a.shape[0]).T # mean center
(w, v) = np.linalg.eig(np.cov(a.T))
i = np.nonzero(
(-w).argsort() == 0)[0][0] # indices for the top 2 eigenvalues
j = np.nonzero((-w).argsort() == 1)[0][0]
xy = (np.mean(x), np.mean(y))
width = np.sqrt(w[i]) * 2
height = np.sqrt(w[j]) * 2
unit = np.array([1, 0])
vi = v[:, i]
#vi = vi / norm(vi) # already normalized
theta = utils.get_theta(vi, unit)
angle = utils.rad_to_deg(theta)
return (xy, width, height, angle)
def smpl_plot():
smpl = smpl_np.SMPLModel('../smpl/model.pkl')
np.random.seed(9608)
pose = get_theta('00000')
beta = get_beta('00000')
trans = get_trans('00000')
smpl.set_params(beta=beta, pose=pose, trans=trans)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
mesh = Poly3DCollection(smpl.verts[smpl.faces], alpha=0.05)
mesh.set_edgecolor((0.3,0.3,0.3))
mesh.set_facecolor((0.7,0.7,0.7))
ax.add_collection3d(mesh)
J = smpl.J_regressor.dot(smpl.verts)
print(J)
print(J.shape, type(J))
print(smpl.J_regressor.shape)
for j in range(len(J)):
pos1 = J[j]
ax.scatter3D([pos1[0]], [pos1[1]], [pos1[2]], label=f"{j}")
plt.legend()
plt.show()
def main(img_path, json_path=None):
input_img, proc_param, img = preprocess_image(img_path, json_path)
# Add batch dimension: 1 x D x D x 3
input_img = np.expand_dims(input_img, 0)
# Theta is the 85D vector holding [camera, pose, shape]
# where camera is 3D [s, tx, ty]
# pose is 72D vector holding the rotation of 24 joints of SMPL in axis angle format
# shape is 10D shape coefficients of SMPL
device = torch.device('cuda', 0)
smpl = SMPL('../../smpl/model_cocoplus.pkl', obj_saveable=True).to(device)
pose = get_theta('00001')
beta = get_beta('00001')
vbeta = torch.tensor(np.array([beta])).float().to(device)
vpose = torch.tensor(np.array([pose])).float().to(device)
vcam = torch.tensor([0.9, 0, 0]).expand((1, 3)).float().to(device)
verts, joints, _ = smpl(vbeta, vpose, get_skin=True)
pred_kp = batch_orth_proj_idrot(joints.cpu(), vcam.cpu())
r = torch.ones((1, 3))
verts, joints, r = verts.cpu().numpy(), pred_kp.cpu().numpy(), vcam.cpu(
).numpy()
print(img.shape)
print(type(joints), type(verts), type(r))
print(joints[0].shape, verts[0].shape, r[0].shape)
visualize(img, proc_param, joints[0], verts[0], r[0])
joint_y = torch.matmul(verts[:, :, 1], self.joint_regressor.t())
joint_z = torch.matmul(verts[:, :, 2], self.joint_regressor.t())
joints = torch.stack([joint_x, joint_y, joint_z], dim=2)
if get_skin:
return verts, joints, Rs
else:
return joints
if __name__ == '__main__':
device = torch.device('cuda', 0)
smpl = SMPL('../../smpl/model_cocoplus.pkl', obj_saveable=True).to(device)
pose = get_theta('00002')
pose[:3] = 0
beta = get_beta('00002')
cam = np.array([0.9, 0, 0])
vbeta = torch.tensor(np.array([beta])).float().to(device)
vpose = torch.tensor(np.array([pose])).float().to(device)
vcam = torch.tensor(np.array([cam])).float().to(device)
verts, j, r = smpl(vbeta, vpose, get_skin=True)
smpl.save_obj(verts[0].cpu().numpy(), './mesh.obj')
verts_numpy = verts.cpu().numpy()
faces_numpy = smpl.faces
np.save('../../resault/verts.npy', verts_numpy)
np.save('../../resault/faces.npy', faces_numpy)
def b_n(k, s):
return( get_lambda(S)+get_mu()+k*get_theta(S)+s )
def a_n(k):
return( -get_lambda(S) * (get_mu()+k*get_theta(S)) )