def set_look_at_parameters(self, azimuth, elevation, distance):
camera_projection_mtx = perspectiveprojectionnp(self.camera_fov_y, 1.0)
camera_projection_mtx = torch.FloatTensor(camera_projection_mtx).cuda()
camera_view_mtx = []
camera_view_shift = []
for a, e, d in zip(azimuth, elevation, distance):
mat, pos = compute_camera_params(a, e, d)
camera_view_mtx.append(mat)
camera_view_shift.append(pos)
camera_view_mtx = torch.stack(camera_view_mtx).cuda()
camera_view_shift = torch.stack(camera_view_shift).cuda()
self.camera_params = [
camera_view_mtx, camera_view_shift, camera_projection_mtx
]
coef = 5
p = p * coef
##########################################################
campos = np.array([0, 0, 1.5], dtype=np.float32) # where camera it is
camcenter = np.array([0, 0, 0],
dtype=np.float32) # where camra is looking at
camup = np.array([-1, 1, 0], dtype=np.float32) # y axis of camera view
camviewmtx, camviewshift = lookatnp(campos.reshape(3, 1),
camcenter.reshape(3, 1),
camup.reshape(3, 1))
camviewshift = -np.dot(camviewmtx.transpose(), camviewshift)
camfovy = 45 / 180.0 * np.pi
camprojmtx = perspectiveprojectionnp(camfovy, 1.0 * 1.0 / 1.0)
#####################################################
tfp_px3 = torch.from_numpy(p)
tfp_px3.requires_grad = True
tff_fx3 = torch.from_numpy(f)
tfuv_tx2 = torch.from_numpy(uv)
tfuv_tx2.requires_grad = True
tfft_fx3 = torch.from_numpy(ft)
tftex_thxtwx3 = torch.from_numpy(np.ascontiguousarray(texturenp))
tftex_thxtwx3.requires_grad = True
tfcamviewmtx = torch.from_numpy(camviewmtx)
images_set_valid = kal.dataloader.ShapeNet.Images(root ='../../datasets/',categories =args.categories , \
download = True, train = False, split = .7, views=1, transform= preprocess )
dataloader_val = DataLoader(images_set_valid,
batch_size=args.batchsize,
shuffle=False,
num_workers=8)
"""
Model settings
"""
mesh = kal.rep.TriangleMesh.from_obj('386.obj', enable_adjacency=True)
mesh.cuda()
normalize_adj(mesh)
initial_verts = mesh.vertices.clone()
camera_fov_y = 49.13434207744484 * np.pi / 180.0
cam_proj = perspectiveprojectionnp(camera_fov_y, 1.0)
cam_proj = torch.FloatTensor(cam_proj).cuda()
model = Encoder(4, 5, args.batchsize, 137, mesh.vertices.shape[0]).cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
renderer = Dib_Renderer(137, 137, mode='VertexColor')
# Create log directory, if it doesn't already exist
args.logdir = os.path.join(args.logdir, args.expid)
if not os.path.isdir(args.logdir):
os.makedirs(args.logdir)
print('Created dir:', args.logdir)
# Log all commandline args
with open(os.path.join(args.logdir, 'args.txt'), 'w') as f: