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]
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
save_obj_mesh_with_color(save_path, verts, faces, color)
except Exception as e:
print(e)
def gen_mesh_imgColor(res, netMR, netC, 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)
netMR.filter_global(image_tensor_global)
netMR.filter_local(image_tensor[:,None])
netC.filter(image_tensor_global)
netC.attach(netMR.netG.get_im_feat())
try:
if netMR.netG.netF is not None:
image_tensor_global = torch.cat([image_tensor_global, netMR.netG.nmlF], 0)
if netMR.netG.netB is not None:
image_tensor_global = torch.cat([image_tensor_global, netMR.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(
netMR, 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()
color = np.zeros(verts.shape)
interval = 10000
for i in range(len(color) // interval):
left = i * interval
right = i * interval + interval
if i == len(color) // interval - 1:
right = -1
netC.query(verts_tensor[:, :, left:right], calib_tensor)
rgb = netC.get_preds()[0].detach().cpu().numpy() * 0.5 + 0.5
color[left:right] = rgb.T
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_256'].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, :]
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)
