def viewer(data_path):
pointcloud_list = DataProcessing.get_pointcloud_list_semantickitti(
data_path)
label_list = DataProcessing.get_label_list_semantickitti(data_path)
colors = Plot.random_colors(21, seed=2)
for index in range(len(pointcloud_list)):
pointcloud_path = pointcloud_list[index]
label_path = label_list[index]
pointcloud = DataProcessing.load_pc_kitti(pointcloud_path)
label = DataProcessing.load_label_kitti(label_path, remap_lut)
pointcloud_withlabel = np.zeros((pointcloud.shape[0], 6), dtype=np.int)
pointcloud_withlabel[:, 0:3] = pointcloud
pointcloud_withlabel[:, 5] = 1
Plot.draw_pointcloud(pointcloud, "pointcloud:{}".format(index))
Plot.draw_pointcloud_semantic_instance(
pointcloud, label, "pointcloud_label:{}".format(index))
os.makedirs(pc_path_out) if not os.path.exists(pc_path_out) else None
os.makedirs(
KDTree_path_out) if not os.path.exists(KDTree_path_out) else None
if int(seq_id) < 11:
label_path = os.path.join(seq_path, 'labels')
label_path_out = os.path.join(seq_path_out, 'labels')
os.makedirs(
label_path_out) if not os.path.exists(label_path_out) else None
scan_list = np.sort(os.listdir(pc_path))
for scan_id in scan_list:
print(scan_id)
points = DataProcessing.load_pc_kitti(
os.path.join(pc_path, scan_id))
labels = DataProcessing.load_label_kitti(
os.path.join(label_path,
str(scan_id[:-4]) + '.label'), remap_lut)
sub_points, sub_labels = DataProcessing.grid_sub_sampling(
points, labels=labels, grid_size=sub_grid_size)
search_tree = KDTree(sub_points)
KDTree_save = os.path.join(KDTree_path_out,
str(scan_id[:-4]) + '.pkl')
np.save(os.path.join(pc_path_out, scan_id)[:-4], sub_points)
np.save(os.path.join(label_path_out, scan_id)[:-4], sub_labels)
with open(KDTree_save, 'wb') as f:
pickle.dump(search_tree, f)
if seq_id == '08':
proj_path = os.path.join(seq_path_out, 'proj')
os.makedirs(
proj_path) if not os.path.exists(proj_path) else None
proj_inds = np.squeeze(
def detect_pc(self):
colors = Plot.random_colors(21, seed=2)
# for batch_idx, batch_data in enumerate(self.test_dataloader):
# for key in batch_data:
# if type(batch_data[key]) is list:
# for i in range(len(batch_data[key])):
# batch_data[key][i] = batch_data[key][i].cuda()
# else:
# batch_data[key] = batch_data[key].cuda()
# xyz = batch_data['xyz'] # (batch,N,3)
# neigh_idx = batch_data['neigh_idx'] # (batch,N,16)
# sub_idx = batch_data['sub_idx'] # (batch,N/4,16)
# interp_idx = batch_data['interp_idx'] # (batch,N,1)
# features = batch_data['features'] # (batch, 3, N)
# labels = batch_data['labels'] # (batch, N)
# input_inds = batch_data['input_inds'] # (batch, N)
# cloud_inds = batch_data['cloud_inds'] # (batch, 1)
# with torch.no_grad():
# self.out = self.net(xyz, neigh_idx, sub_idx, interp_idx,
# features, labels, input_inds, cloud_inds)
# Plot.draw_pointcloud(xyz[0].squeeze().cpu().numpy(),
# "pointcloud:{}".format(batch_idx))
# Plot.draw_pointcloud_semantic_instance(
# xyz[0].squeeze().cpu().numpy(),
# labels.cpu().numpy()[0],
# "pointcloud_label:{}".format(batch_idx), colors)
# Plot.draw_pointcloud_semantic_instance(
# xyz[0].squeeze().cpu().numpy(),
# self.out.argmax(dim=1).cpu().numpy().squeeze(),
# "pointcloud_label:{}".format(batch_idx), colors)
# print(self.out.argmax(dim=1).cpu().numpy().squeeze())
# print(labels.cpu().numpy()[0])
for seq_id in sequence_list:
print('sequence' + seq_id + ' start')
seq_path = os.path.join(dataset_path, seq_id)
pc_path = os.path.join(seq_path, 'velodyne')
label_path = os.path.join(seq_path, 'labels')
scan_list = np.sort(os.listdir(pc_path))
for scan_id in scan_list:
print(scan_id)
points = DataProcessing.load_pc_kitti(
os.path.join(pc_path, scan_id))
labels = DataProcessing.load_label_kitti(
os.path.join(label_path,
str(scan_id[:-4]) + '.label'), remap_lut)
# label_ = labels
search_tree = KDTree(points)
pick_idx = np.random.choice(len(points), 1)
print(pick_idx)
selected_pc_, selected_labels_, selected_idx_, cloud_ind_ = [],[],[],[]
# selected_pc, selected_labels, selected_idx = SemanticKITTI.crop_pc(
# points, labels, search_tree, pick_idx)
# selected_pc = selected_pc.astype(np.float32)
# selected_labels = selected_labels.astype(np.int32)
# selected_idx = selected_idx.astype(np.int32)
selected_pc = points.astype(np.float32)
selected_labels = labels.astype(np.int32)
selected_idx = pick_idx.astype(np.int32)
selected_pc_.append(selected_pc) # (N,3)
selected_labels_.append(selected_labels) # (N,)
selected_idx_.append(selected_idx) # (N,)
cloud_ind_.append(np.array([scan_id[:-4]],
dtype=np.int32)) # (1,)
selected_pc_ = np.stack(selected_pc_) # (batch,N,3)
selected_labels_ = np.stack(selected_labels_) # (batch,N)
selected_idx_ = np.stack(selected_idx_) # (batch,N)
cloud_ind_ = np.stack(cloud_ind_) # (batch,1)
flat_inputs = SemanticKITTI.tf_map(selected_pc_,
selected_labels_,
selected_idx_, cloud_ind_)
num_layers = ConfigSemanticKITTI.num_layers
inputs = {}
inputs['xyz'] = [] # (batch,N,3)
for tmp in flat_inputs[:num_layers]:
inputs['xyz'].append(torch.from_numpy(tmp).float().cuda())
inputs['neigh_idx'] = [] # (batch,N,16)
for tmp in flat_inputs[num_layers:2 * num_layers]:
inputs['neigh_idx'].append(
torch.from_numpy(tmp).long().cuda())
inputs['sub_idx'] = [] # (batch,N/4,16)
for tmp in flat_inputs[2 * num_layers:3 * num_layers]:
inputs['sub_idx'].append(
torch.from_numpy(tmp).long().cuda())
inputs['interp_idx'] = [] # (batch,N,1)
for tmp in flat_inputs[3 * num_layers:4 * num_layers]:
inputs['interp_idx'].append(
torch.from_numpy(tmp).long().cuda())
inputs['features'] = torch.from_numpy(
flat_inputs[4 * num_layers]).transpose(
1, 2).float().cuda() # (batch, N, 3)->(batch, 3, N)
inputs['labels'] = torch.from_numpy(
flat_inputs[4 * num_layers +
1]).long().cuda() # (batch, N)
inputs['input_inds'] = torch.from_numpy(
flat_inputs[4 * num_layers +
2]).long().cuda() # (batch, N)
inputs['cloud_inds'] = torch.from_numpy(
flat_inputs[4 * num_layers +
3]).long().cuda() # (batch, 1)
xyz = inputs['xyz'] # (batch,N,3)
neigh_idx = inputs['neigh_idx'] # (batch,N,16)
sub_idx = inputs['sub_idx'] # (batch,N/4,16)
interp_idx = inputs['interp_idx'] # (batch,N,1)
features = inputs['features'] # (batch, 3, N)
labels = inputs['labels'] # (batch, N)
input_inds = inputs['input_inds'] # (batch, N)
cloud_inds = inputs['cloud_inds'] # (batch, 1)
with torch.no_grad():
# labels = labels.reshape(-1)
self.out = self.net(
xyz, neigh_idx, sub_idx, interp_idx, features, labels,
input_inds,
cloud_inds).argmax(dim=1).cpu().numpy().squeeze()
print(self.out)
pred = self.out.astype(np.uint32)
upper_half = pred >> 16
lower_half = pred & 0xFFFF
lower_half = remap_lut[lower_half]
pred = (upper_half << 16) + lower_half
pred = pred.astype(np.uint32)
print(pred)
# logits = self.out.transpose(1, 2).reshape(
# -1, ConfigSemanticKITTI.num_classes)
# ignored_bool = labels == 0
# for ign_label in ConfigSemanticKITTI.ignored_label_inds:
# ignored_bool = ignored_bool | (labels == ign_label)
# valid_idx = ignored_bool == 0
# valid_logits = logits[valid_idx, :]
# valid_labels_init = labels[valid_idx]
# print(valid_logits.shape)
# print(self.out.argmax(dim=1).cpu().numpy().squeeze().shape)
# Plot.draw_pointcloud(xyz[0].squeeze().cpu().numpy(),
# "pointcloud:{}".format(scan_id))
# Plot.draw_pointcloud_semantic_instance(
# xyz[0].squeeze().cpu().numpy(), pred,
# "pointcloud_label:{}".format(scan_id))
# print(self.out.argmax(dim=1).cpu().numpy().squeeze())
print(labels.cpu().numpy()[0])
Plot.draw_pointcloud_semantic_instance(
xyz[0].squeeze().cpu().numpy(),
labels.cpu().numpy()[0],
"pointcloud_label:{}".format(scan_id))