def test():
from svstools import visualization as vis
train_set = S3DISDataset("/home/burak/workspace/seg/data",
split=SPLITS[1]['train'])
data, labels = train_set[213]
pc = data[:3].T
pcd = vis.paint_colormap(pc, labels)
vis.show_pointcloud(pcd, coordinate_frame=[1, [0, 0, 0]])
return
def test2():
np.random.seed(0)
import sys
sys.path.append("/seg")
from datasets.s3dis.dataset import Dataset
dataset = Dataset(split='test', crossval_id=1, num_points=2**17)
data, labels, groups = dataset[5]
length = 128
for h in [5, 3]:
sub_pc, grouping, bboxes = groups[h]
groups_array = []
masks = []
group_features = np.zeros((6, len(grouping), length), dtype=data.dtype)
for i, g in enumerate(grouping):
num_dummy = length - len(g)
# m = np.append(np.ones(len(g), dtype=np.bool), np.zeros(num_dummy, dtype=np.bool))
g = np.append(g, np.repeat(g[0], num_dummy))
group_features[:, i] = data[:, g]
# groups_array.append(g)
# masks.append(m)
# groups_array = np.asarray(groups_array)
# masks = np.asarray(masks)
# for
continue
sub_pc, grouping, bboxes = groups[3]
neighborhood = get_neighborhood(sub_pc.T, k=8)
neighborhood_mesh = draw_neighborhood2(sub_pc[:3].T, neighborhood)
spheres = o3d.geometry.TriangleMesh()
boxes = o3d.geometry.LineSet()
for p, bb in zip(sub_pc.T, bboxes):
spheres += vis.draw_sphere(p[:3], 0.001)
boxes += vis.draw_bbox(bb, color=[0, 0, 1])
vis.show_pointcloud(data.T, [spheres, boxes, neighborhood_mesh])
return
def make_object_grid(objects, grid):
assert np.prod(grid) == len(objects)
# coords = np.array(np.meshgrid(np.arange(grid[0]), np.arange(grid[1])))
# coords = coords.transpose(0, 2, 1).reshape(-1,2)
# coords = np.hstack((coords, np.zeros((len(coords), 1)))).astype(float)
coords = []
for x in range(grid[0]):
for y in range(grid[1]):
coords += [[x, y, 0.0]]
coords = np.array(coords) * 3.0
geometries = []
for obj, coord in zip(objects, coords):
geometries += [obj.translate(coord)]
vis.show_pointcloud([], geometries)
return
def get_modelnet_grid():
from utils.datasets import modelnet
sets = modelnet.get_sets("data/modelnet")
train_set = sets[0]
labels = train_set.label.reshape(-1)
objects = []
for c in [7]:
print(modelnet40_objects[c])
i = np.where(labels == c)[0][2]
pc = train_set.data[i]
pcd = pc_utils.points2PointCloud(pc)
vis.show_pointcloud(pcd)
pcd = pcd.rotate([0,30,0])
objects += [pcd]
make_object_grid(objects, [5, 8])
def draw_boxes(pc, coordinates, box_size=0.2, point_limit=2**15):
c = coordinates[..., np.newaxis]
bboxes = np.concatenate((c, c + box_size), axis=-1).reshape(-1, 6)
sub_pc = c.reshape(-1, 3) + box_size / 2
neighborhood = get_neighborhood(sub_pc, k=8)
neighborhood_mesh = draw_neighborhood(sub_pc, neighborhood)
spheres = o3d.geometry.TriangleMesh()
boxes = o3d.geometry.LineSet()
for p, bb in zip(sub_pc, bboxes):
spheres += vis.draw_sphere(p[:3], 0.001)
boxes += vis.draw_bbox(bb, color=[0, 0, 1])
if point_limit > 0:
pc = pc[np.random.permutation(len(pc))[:point_limit]]
vis.show_pointcloud(pc, [spheres, boxes, neighborhood_mesh])
def test():
from svstools import visualization as vis
print("loading dataset")
dataloader = ScanNetDataset('data/scannet', split='train')
scene = dataloader.loaded["test/captures/scene0707_00.npy"]
num_points = 2**17
# Get target indices
indices = np.arange(len(scene))
if len(scene) < num_points:
indices = np.append(
indices,
np.random.permutation(len(scene))[:len(scene) - num_points])
np.random.shuffle(indices)
scene = scene[indices, :6]
vis.show_pointcloud(scene)
def draw_room(cloud, labels=None, mode="point"):
# indices = np.random.permutation(len(cloud))[:2**15]
# cloud = cloud[indices]
# labels = labels[indices]
color_map = [vis.get_color(i, N=13) for i in range(len(categories))]
if labels is not None:
colors = np.asarray([color_map[l] for l in labels])
elif cloud.shape[1] == 6:
colors = cloud[:, 3:6]
else:
raise ValueError("No color information provided!")
if mode == "sphere":
spheres = vis.o3d.geometry.TriangleMesh()
for p, c in zip(cloud, colors):
spheres += vis.draw_sphere(p, radius=0.01, color=c)
vis.show_pointcloud(np.array([[0, 0, 0]]), geometries=[spheres])
elif mode == "point":
painted = np.column_stack((cloud, colors))
vis.show_pointcloud(painted)
def test():
from svstools import visualization as vis
# loaded = np.loadtxt("/home/burak/datasets/Stanford3d_batch_version/Area_1/office_1/Batch_Folder/batch_10.txt")
# pc, labels = loaded[:, :3], loaded[:, -1].astype(np.int32)
loaded = np.load("augmentations_test.npz")
pc, labels = loaded["pc"], loaded["labels"]
labels += 1
pcd_colored_org = vis.pc_utils.points2PointCloud(
vis.paint_colormap(pc, labels))
cf_org = vis.o3d.geometry.TriangleMesh.create_coordinate_frame(
size=0.5, origin=[0, 0, 0])
target_augmentations = [
["rotate_point_cloud", rotate_point_cloud, ()],
["shift_point_cloud", shift_point_cloud, ()],
["jitter_point_cloud", jitter_point_cloud, ()],
["random_scale_point_cloud", random_scale_point_cloud, ()],
]
shift = 4
batch = pc[np.newaxis, ...]
for name, aug, args in target_augmentations:
print("Augmentation: %s" % (name))
pc = aug(batch.copy(), *args)[0]
pc[:, 0] += shift
pcd_colored = vis.pc_utils.points2PointCloud(
vis.paint_colormap(pc, labels))
cf = vis.o3d.geometry.TriangleMesh.create_coordinate_frame(
size=0.5, origin=[shift, 0, 0])
geometries = [cf, cf_org, pcd_colored_org]
vis.show_pointcloud(pcd_colored, geometries=geometries)
def main():
file_path = "/seg/scripts/output.npz"
npz = np.load(file_path, allow_pickle=True)
cloud = npz["cloud"]
preds = npz["preds"]
labels = npz["labels"]
# d = dataset.Dataset(split="test", crossval_id=1)
# cloud, gt, meta = d[0]
pc = cloud[0].T
p, l = preds.argmax(axis=-2)[0], labels[0]
pc = vis.paint_colormap(pc, p)
# pc = pc_utils.pointCloud2Points(pcd)
bbox = np.row_stack(
(pc[:, :3].min(axis=0), pc[:, :3].max(axis=0))).T.reshape(-1)
bbox[-1] -= 0.05
# pc = pc[pc_utils.crop_bbox(pc, bbox)]
vis.show_pointcloud(pc, coordinate_frame=(0.01, (0, 0, 0)))