def gen_mesh(res,
net,
cuda,
data,
save_path,
thresh=0.5,
use_octree=True,
components=False):
image_tensor_global = data['img_512'].to(device=cuda)
image_tensor = data['img'].to(device=cuda)
calib_tensor = data['calib'].to(device=cuda)
net.filter_global(image_tensor_global)
net.filter_local(image_tensor[:, None])
try:
if net.netG.netF is not None:
image_tensor_global = torch.cat(
[image_tensor_global, net.netG.nmlF], 0)
if net.netG.netB is not None:
image_tensor_global = torch.cat(
[image_tensor_global, net.netG.nmlB], 0)
except:
pass
b_min = data['b_min']
b_max = data['b_max']
try:
save_img_path = save_path[:-4] + '.png'
save_img_list = []
for v in range(image_tensor_global.shape[0]):
save_img = (
np.transpose(image_tensor_global[v].detach().cpu().numpy(),
(1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0
save_img_list.append(save_img)
save_img = np.concatenate(save_img_list, axis=1)
cv2.imwrite(save_img_path, save_img)
verts, faces, _, _ = reconstruction(net,
cuda,
calib_tensor,
res,
b_min,
b_max,
thresh,
use_octree=use_octree,
num_samples=50000)
verts_tensor = torch.from_numpy(
verts.T).unsqueeze(0).to(device=cuda).float()
# if 'calib_world' in data:
# calib_world = data['calib_world'].numpy()[0]
# verts = np.matmul(np.concatenate([verts, np.ones_like(verts[:,:1])],1), inv(calib_world).T)[:,:3]
#print(calib_tensor)
color = np.zeros(verts.shape)
interval = 50000
for i in range(len(color) // interval + 1):
left = i * interval
if i == len(color) // interval:
right = -1
else:
right = (i + 1) * interval
net.calc_normal(verts_tensor[:, None, :, left:right],
calib_tensor[:, None], calib_tensor)
nml = net.nmls.detach().cpu().numpy()[0] * 0.5 + 0.5
color[left:right] = nml.T
if 'calib_world' in data:
calib_world = data['calib_world'].numpy()[0]
verts = np.matmul(
np.concatenate([verts, np.ones_like(verts[:, :1])], 1),
inv(calib_world).T)[:, :3]
save_obj_mesh_with_color(save_path, verts, faces, color)
except Exception as e:
print(e)
def gen_mesh_imgColor(res,
net,
cuda,
data,
save_path,
thresh=0.5,
use_octree=True,
components=False):
image_tensor_global = data['img_512'].to(device=cuda)
image_tensor = data['img'].to(device=cuda)
calib_tensor = data['calib'].to(device=cuda)
net.filter_global(image_tensor_global)
net.filter_local(image_tensor[:, None])
try:
if net.netG.netF is not None:
image_tensor_global = torch.cat(
[image_tensor_global, net.netG.nmlF], 0)
if net.netG.netB is not None:
image_tensor_global = torch.cat(
[image_tensor_global, net.netG.nmlB], 0)
except:
pass
b_min = data['b_min']
b_max = data['b_max']
try:
save_img_path = save_path[:-4] + '.png'
save_img_list = []
for v in range(image_tensor_global.shape[0]):
save_img = (
np.transpose(image_tensor_global[v].detach().cpu().numpy(),
(1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0
save_img_list.append(save_img)
save_img = np.concatenate(save_img_list, axis=1)
cv2.imwrite(save_img_path, save_img)
verts, faces, _, _ = reconstruction(net,
cuda,
calib_tensor,
res,
b_min,
b_max,
thresh,
use_octree=use_octree,
num_samples=100000)
verts_tensor = torch.from_numpy(
verts.T).unsqueeze(0).to(device=cuda).float()
# if this returns error, projection must be defined somewhere else
xyz_tensor = net.projection(verts_tensor, calib_tensor[:1])
uv = xyz_tensor[:, :2, :]
print('uv', uv)
print('xyz_tensor', xyz_tensor)
print('verts', verts)
color = index(image_tensor[:1], uv).detach().cpu().numpy()[0].T
color = color * 0.5 + 0.5
if 'calib_world' in data:
calib_world = data['calib_world'].numpy()[0]
verts = np.matmul(
np.concatenate([verts, np.ones_like(verts[:, :1])], 1),
inv(calib_world).T)[:, :3]
save_obj_mesh_with_color(save_path, verts, faces, color)
except Exception as e:
print(e)
