def visualize_layers(reachable_layers):
layers = {}
for (x, y, z), label in label_matrix.voxels.items():
pos = Vec3(x, y, z)
if label == 0 or label not in reachable_layers:
continue
if label in layers:
layers[label][pos] = True
continue
layers[label] = np.zeros(label_matrix.shape, dtype=bool)
layers[label][pos] = True
color_gen = randomcolor.RandomColor()
colors = color_gen.generate(count=len(layers), format_='rgb')
for i in range(len(colors)):
values = map(lambda e: int(e), re.findall('\\d+', colors[i]))
values = list(values)
colors[i] = (values[0] / 255, values[1] / 255, values[2] / 255)
meshes = []
i = 0
for label in layers:
color = colors[i]
meshes.append(Mesh.from_voxel_grid(layers[label], colors=color))
i += 1
show(meshes)
dphi, dtheta = np.pi / 250.0, np.pi / 250.0
[phi, theta] = np.mgrid[0:np.pi + dphi * 1.5:dphi,
0:2 * np.pi + dtheta * 1.5:dtheta]
m0 = 4
m1 = 3
m2 = 2
m3 = 3
m4 = 6
m5 = 2
m6 = 6
m7 = 4
r = np.sin(m0 * phi)**m1 + np.cos(m2 * phi)**m3
r = r + np.sin(m4 * theta)**m5 + np.cos(m6 * theta)**m7
x = r * np.sin(phi) * np.cos(theta)
y = r * np.cos(phi)
z = r * np.sin(phi) * np.sin(theta)
m = Mesh.from_xyz(x, y, z)
show(m,
camera_position=(0, 2.32, 2.47),
up_vector=(-0.7265758, -0.35081482, 0.12523057),
behaviours=[SnapshotOnKey()],
size=(256, 256))
# It is also possible to load a mesh with a colormap
m = Mesh.from_xyz(x, y, z, colormap=plt.cm.jet)
show(m,
camera_position=(0, 2.32, 2.47),
up_vector=(-0.7265758, -0.35081482, 0.12523057),
behaviours=[SnapshotOnKey()],
size=(256, 256))
[-0.08633196, -0.02354074, 0.02689737],
[0.07980453, 0.08336258, -0.03348995],
[0.00127693, 0.08648632, -0.06349777],
[-0.00372324, 0.06101485, 0.05373947],
[0.02931256, 0.13969943, 0.04758224],
[-0.07085561, 0.1854981, -0.01251608],
[-0.08501478, 0.08730197, -0.03007746],
[-0.2747325, 0.11887977, -0.02393699],
[-0.18173946, 0.19762556, -0.04584916],
[-0.00075296, 0.44299334, -0.00646337],
[0.00776821, 0.36928397, -0.01387711],
[0.00775032, 0.27719203, -0.02083887],
[-0.07311475, 0.2800464, -0.05349565],
[0.09813186, 0.2514349, -0.05162171],
[0.06995263, 0.22113597, 0.02499798],
[0.2917657, 0.11937696, 0.00506067],
[0.21196616, 0.17858423, -0.02221516],
])
s = Spherecloud(points, colors=(0.8, 0.2, 0.4), sizes=0.03)
l = Lines(points, (0.1, 0.1, 0.1), width=0.02)
show([s, l],
up_vector=(0, 1.0, 0),
camera_position=(0, 0, 1.5),
size=(256, 256),
behaviours=[
CameraTrajectory(Circle((0, 0, 0), (0, 0.0, 1.5), (0, 1.0, 0)),
speed=0.01),
LightToCamera()
])
link = abs(x - y) + abs(y - z) + abs(z - x) <= 2
# Combine the objects into a single boolean array
voxels = cube1 | cube2 | link
# Set the colors of each object
colors = np.empty(voxels.shape + (3,), dtype=np.float32)
colors[link] = (1, 0, 0)
colors[cube1] = (0, 0, 1)
colors[cube2] = (0, 1, 0)
show(
Mesh.from_voxel_grid(voxels=voxels, colors=colors),
light=(-1, -1, 1),
behaviours=[
CameraTrajectory(
Circle(center=(0, 0, 0), point=(2, -1, 0), normal=(0, 0, -1)),
speed=0.004)
]
)
# Render scene to file
# render(
# Mesh.from_voxel_grid(voxels=voxels, colors=colors),
# n_frames=256,
# light=(-1, -1, 1),
# behaviours=[
# CameraTrajectory(
# Circle(center=(0, 0, 0), point=(2, -1, 0), normal=(0, 0, -1)),
# speed=0.004
# ),
from simple_3dviz.behaviours.movements import CameraTrajectory, LightTrajectory
from simple_3dviz.behaviours.trajectory import Circle, Repeat, BackAndForth, \
Lines, QuadraticBezierCurves
from simple_3dviz.window import show
if __name__ == "__main__":
# Make a random point cloud
centers = np.random.randn(30, 3)
colors = np.array([[1., 0, 0, 1], [0, 1, 1,
1]])[np.random.randint(0, 2, size=30)]
sizes = np.ones(30) * 0.2
# Move in a circle around the points
show(Spherecloud(centers, colors, sizes),
behaviours=[
CameraTrajectory(Circle([0, 0, 3], [3, 3, 3], [0, 0, 1]),
speed=0.001),
LightToCamera(offset=[-1, -1, 0])
])
# Move in an endless square
show(Spherecloud(centers, colors, sizes),
behaviours=[
CameraTrajectory(Repeat(
Lines([-4, -4, 1], [-4, 4, 1], [4, 4, 1], [4, -4, 1],
[-4, -4, 1])),
speed=0.001),
LightToCamera(offset=[-1, -1, 0])
])
# Move back and forth on a line
show(Spherecloud(centers, colors, sizes),
def sign_unsigned(fname='./data/elephant.pwn',
K=5,
ndisc=50,
debug_viz=True,
R=15,
cache_sign=False,
use_sign_cache=False):
points, tetvertices, trivertices, is_in_band, epsilon, dist = section1(
fname, K, ndisc)
print('Chosen Epsilon:', epsilon.item())
# coarse sign estimate
print('sign estimate...')
v2v = get_v2v(tetvertices.numpy())
outside_ixes = torch.arange(len(dist))[dist >= epsilon]
is_in_band = dist < epsilon
if use_sign_cache:
print('using sign cache...')
with open('sign_cache.json', 'r') as reader:
guesses = json.load(reader, parse_int=int)
guesses = {int(k): v for k, v in guesses.items()}
else:
guesses = {}
for ix in tqdm(outside_ixes.numpy()):
guesses[ix] = estimate_sign(ix.item(), points.numpy(), v2v,
is_in_band.numpy(), R)
if cache_sign:
print('caching sign...')
guesses = {int(k): [int(v) for v in vec] for k, vec in guesses.items()}
with open('sign_cache.json', 'w') as writer:
json.dump(guesses, writer)
print(guesses)
# cross-sections of the sign plot
alldata = []
for v, changes in guesses.items():
coords = points[v]
vals = [val % 2 for val in changes if val > 0]
if len(vals) > 0:
sign = 1 if np.mean(vals) > 0.5 else 0
alldata.append(
[coords[0], coords[1], coords[2],
np.mean(vals), sign])
alldata = np.array(alldata)
spec = np.sort(np.unique(alldata[:, 2]))
for spei, spe in enumerate(spec):
kept = alldata[:, 2] == spe
fig = plt.figure()
gs = mpl.gridspec.GridSpec(1, 2)
ax = plt.subplot(gs[0, 0])
plt.scatter(alldata[kept, 0],
alldata[kept, 1],
c=alldata[kept, 3],
vmin=0,
vmax=1,
cmap='winter')
ax.set_aspect('equal')
plt.xlim((np.min(alldata[:, 0]), np.max(alldata[:, 0])))
plt.ylim((np.min(alldata[:, 1]), np.max(alldata[:, 1])))
ax = plt.subplot(gs[0, 1])
plt.scatter(alldata[kept, 0],
alldata[kept, 1],
c=alldata[kept, 4],
vmin=0,
vmax=1,
cmap='winter')
ax.set_aspect('equal')
plt.xlim((np.min(alldata[:, 0]), np.max(alldata[:, 0])))
plt.ylim((np.min(alldata[:, 1]), np.max(alldata[:, 1])))
imname = f'out{spei:04}.jpg'
print('saving', imname)
plt.savefig(imname)
plt.close(fig)
# visualize coarse mesh
kept = (dist[trivertices[:, 0]] < epsilon) & (dist[
trivertices[:, 1]] < epsilon) & (dist[trivertices[:, 2]] < epsilon)
m = Mesh.from_faces(points.numpy(),
trivertices.numpy()[kept],
colors=np.ones((len(points), 3)) * [1.0, 0.0, 0.0])
show(m)
def main(argv):
parser = argparse.ArgumentParser(
description="Do the forward pass and visualize the recovered parts")
parser.add_argument(
"config_file",
help="Path to the file that contains the experiment configuration")
parser.add_argument("--output_directory",
default="../demo/output/",
help="Save the output files in that directory")
parser.add_argument(
"--weight_file",
default=None,
help=("The path to a previously trained model to continue"
" the training from"))
parser.add_argument("--prediction_file",
default=None,
help="The path to the predicted primitives")
parser.add_argument("--background",
type=lambda x: list(map(float, x.split(","))),
default="1,1,1,1",
help="Set the background of the scene")
parser.add_argument("--up_vector",
type=lambda x: tuple(map(float, x.split(","))),
default="0,0,1",
help="Up vector of the scene")
parser.add_argument("--camera_target",
type=lambda x: tuple(map(float, x.split(","))),
default="0,0,0",
help="Set the target for the camera")
parser.add_argument("--camera_position",
type=lambda x: tuple(map(float, x.split(","))),
default="-2.0,-2.0,-2.0",
help="Camera position in the scene")
parser.add_argument("--window_size",
type=lambda x: tuple(map(int, x.split(","))),
default="512,512",
help="Define the size of the scene and the window")
parser.add_argument("--with_rotating_camera",
action="store_true",
help="Use a camera rotating around the object")
parser.add_argument("--mesh",
action="store_true",
help="Visualize the target mesh")
parser.add_argument("--n_vertices",
type=int,
default=10000,
help="How many vertices to use per part")
add_dataset_parameters(parser)
args = parser.parse_args(argv)
if torch.cuda.is_available():
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
print("Running code on", device)
# Check if output directory exists and if it doesn't create it
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
config = load_config(args.config_file)
# Extract the number of primitives
n_primitives = config["network"]["n_primitives"]
# Dictionary to keep the predictions used for the evaluation
predictions = {}
if args.prediction_file is None:
# Create a dataset instance to generate the input samples
dataset_directory = config["data"]["dataset_directory"]
dataset_type = config["data"]["dataset_type"]
train_test_splits_file = config["data"]["splits_file"]
dataset = dataset_factory(
config["data"]["dataset_factory"],
(ModelCollectionBuilder(config).with_dataset(dataset_type).
filter_category_tags(args.category_tags).filter_tags(
args.model_tags).random_subset(
args.random_subset).build(dataset_directory)),
)
assert len(dataset) == 1
# Build the network architecture to be used for training
network, _, _ = build_network(config, args.weight_file, device=device)
network.eval()
# Create the prediction input
with torch.no_grad():
for sample in dataset:
sample = [s[None] for s in sample] # make a batch dimension
X = sample[0].to(device)
targets = [yi.to(device) for yi in sample[1:]]
F = network.compute_features(X)
phi_volume, _ = network.implicit_surface(F, targets[0])
y_pred, faces = network.points_on_primitives(
F,
args.n_vertices,
random=False,
mesh=True,
union_surface=False)
predictions["phi_volume"] = phi_volume
predictions["y_prim"] = y_pred
else:
preds = torch.load(args.prediction_file, map_location="cpu")
y_pred = preds[4]
faces = preds[5]
targets = preds[0]
predictions["phi_volume"] = preds[1]
predictions["y_prim"] = y_pred
# Get the renderables from the deformed vertices and faces
vertices = y_pred.detach()
parts = range(n_primitives)
renderables = [
Mesh.from_faces(vertices[0, :, i], faces, colors=get_colors(i))
for i in parts
]
behaviours = [
SceneInit(
scene_init(
load_ground_truth(dataset) if args.mesh else None,
args.up_vector, args.camera_position, args.camera_target,
args.background)),
LightToCamera(),
]
if args.with_rotating_camera:
behaviours += [
CameraTrajectory(Circle(args.camera_target, args.camera_position,
args.up_vector),
speed=1 / 180)
]
show(renderables,
size=args.window_size,
behaviours=behaviours + [SnapshotOnKey()])
print("Saving renderables to file")
for i in range(n_primitives):
m = trimesh.Trimesh(vertices[0, :, i].detach(), faces)
m.export(os.path.join(args.output_directory,
"part_{:03d}.obj".format(i)),
file_type="obj")
reso = []
for i in (1, 2, 3):
reso.append(int(first_line[i]))
wx, wy, wz = reso
longest_axis = max(reso)
arr_vox = np.fromfile(args.rawfile, dtype=np.uint8).astype(np.bool)
arr_vox_3d = np.reshape(arr_vox, (wx, wy, wz), order='F')
# bug in simple_3dviz, the spacing is not uniform if the voxel array is not uniform
arr_vox_3d = np.concatenate(
(arr_vox_3d, np.zeros((longest_axis - wx, wy, wz), dtype=np.bool)),
axis=0)
arr_vox_3d = np.concatenate(
(arr_vox_3d,
np.zeros((longest_axis, longest_axis - wy, wz), dtype=np.bool)),
axis=1)
arr_vox_3d = np.concatenate(
(arr_vox_3d,
np.zeros(
(longest_axis, longest_axis, longest_axis - wz), dtype=np.bool)),
axis=2)
half_edge = ((1 / longest_axis) * 0.5, (1 / longest_axis) * 0.5,
(1 / longest_axis) * 0.5)
show(Mesh.from_voxel_grid(voxels=arr_vox_3d,
colors=(0.75, 0.75, 0.75),
sizes=half_edge),
behaviours=[LightToCamera()],
size=(1024, 1024))
def test_line(self):
points = np.array([[-0.5, 0.5, -0.5], [0.5, 0.5, 0.5]])
colors = np.array([[0., 0., 0., 1.], [0., 0., 0., 1.]])
show(Lines(points, colors, width=0.1))
def main(argv):
parser = argparse.ArgumentParser(
description="Do the forward pass and estimate a set of primitives")
parser.add_argument(
"config_file",
help="Path to the file that contains the experiment configuration")
parser.add_argument("output_directory",
help="Save the output files in that directory")
parser.add_argument(
"--weight_file",
default=None,
help="The path to the previously trainined model to be used")
parser.add_argument("--run_on_gpu", action="store_true", help="Use GPU")
parser.add_argument(
"--qos_threshold",
default=1.0,
type=float,
help="Split primitives if predicted qos less than this threshold")
parser.add_argument(
"--vol_threshold",
default=0.0,
type=float,
help="Discard primitives with volume smaller than this threshold")
parser.add_argument(
"--prob_threshold",
default=0.0,
type=float,
help="Discard primitives with probability smaller than this threshold")
parser.add_argument("--with_post_processing",
action="store_true",
help="Remove overlapping primitives")
parser.add_argument("--mesh",
type=load_ground_truth,
help="File of ground truth mesh")
parser.add_argument("--save_frames",
help="Path to save the visualization frames to")
parser.add_argument("--without_screen",
action="store_true",
help="Perform no screen rendering")
parser.add_argument("--n_frames",
type=int,
default=200,
help="Number of frames to be rendered")
parser.add_argument("--background",
type=lambda x: list(map(float, x.split(","))),
default="0,0,0,1",
help="Set the background of the scene")
parser.add_argument("--up_vector",
type=lambda x: tuple(map(float, x.split(","))),
default="0,0,1",
help="Up vector of the scene")
parser.add_argument("--camera_target",
type=lambda x: tuple(map(float, x.split(","))),
default="0,0,0",
help="Set the target for the camera")
parser.add_argument("--camera_position",
type=lambda x: tuple(map(float, x.split(","))),
default="-2.0,-2.0,-2.0",
help="Camer position in the scene")
parser.add_argument("--max_depth",
type=int,
default=3,
help="Maximum depth to visualize")
parser.add_argument("--window_size",
type=lambda x: tuple(map(int, x.split(","))),
default="512,512",
help="Define the size of the scene and the window")
parser.add_argument(
"--from_fit",
action="store_true",
help="Visulize everything based on primitive_params.fit")
parser.add_argument(
"--from_flat_partition",
action="store_true",
help=("Visulize everything based on primitive_params.space_partition"
" with a single depth"))
parser.add_argument("--group_color",
action="store_true",
help="Color the active prims based on the group")
parser.add_argument("--with_rotating_camera",
action="store_true",
help="Use a camera rotating around the object")
parser.add_argument(
"--visualize_sharpness",
action="store_true",
help="When set visualize the sharpness together with the prediction")
add_dataset_parameters(parser)
args = parser.parse_args(argv)
# Check if output directory exists and if it doesn't create it
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
if args.run_on_gpu and torch.cuda.is_available():
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
print("Running code on", device)
config = load_config(args.config_file)
dataloader, network = build_dataloader_and_network_from_args(args,
config,
device=device)
for sample in dataloader:
# Do the forward pass and estimate the primitive parameters
X = sample[0].to(device)
y_hat = network(X)
#import matplotlib.pyplot as plt
#import seaborn as sns
#import numpy as np
#f = plt.figure(figsize=(8, 6))
#sns.barplot(
# np.arange(y_hat.n_primitives),
# y_hat.sharpness_r.squeeze(0).detach().numpy()[:, 0]
#)
#plt.title("Epoch {}".format(args.weight_file.split("/")[-1].split("_")[-1]))
#plt.ylim([0, 10.5])
#plt.ylabel("Sharpness")
#plt.xlabel("Primitive id")
#plt.savefig("/tmp/sharpness_{:03d}.png".format(
# int(args.weight_file.split("/")[-1].split("_")[-1]))
#)
#plt.close()
renderables, active_prims = get_renderables(y_hat, args)
with open(os.path.join(args.output_directory, "renderables.pkl"),
"wb") as f:
pickle.dump(renderables, f)
print(active_prims)
behaviours = [
SceneInit(
scene_init(args.mesh, args.up_vector, args.camera_position,
args.camera_target, args.background)),
LightToCamera(),
]
if args.with_rotating_camera:
behaviours += [
CameraTrajectory(Circle(args.camera_target,
args.camera_position, args.up_vector),
speed=1 / 180)
]
if args.without_screen:
behaviours += [
SaveFrames(args.save_frames, 2),
SaveGif("/tmp/out.gif", 2)
]
render(renderables,
size=args.window_size,
behaviours=behaviours,
n_frames=args.n_frames)
else:
behaviours += [
SnapshotOnKey(path=args.save_frames, keys={"<ctrl>", "S"})
]
show(renderables, size=args.window_size, behaviours=behaviours)
# Based on the active primitives report the metrics
active_primitive_params = \
get_primitive_parameters_from_indices(y_hat, active_prims, args)
report_metrics(active_primitive_params, config,
config["data"]["dataset_type"], args.model_tags,
config["data"]["dataset_directory"])
if args.with_post_processing:
indices = get_non_overlapping_primitives(y_hat, active_prims)
else:
indices = None
for i, m in enumerate(sq_meshes(y_hat, indices)):
m.export(os.path.join(args.output_directory,
"predictions-{}.ply").format(i),
file_type="ply")
if y_hat.space_partition is not None:
torch.save([y_hat.space_partition, y_hat.fit],
os.path.join(args.output_directory,
"space_partition.pkl"))
if args.visualize_sharpness:
visualize_sharpness(
y_hat.sharpness_r.squeeze(0).detach().numpy()[:, 0],
int(args.weight_file.split("/")[-1].split("_")[-1]))
import numpy as np
from simple_3dviz import Mesh, Lines
from simple_3dviz.window import show
def heart_voxel_grid(N):
"""Create a NxNxN voxel grid with True if the voxel is inside a heart
object and False otherwise."""
x = np.linspace(-1.3, 1.3, N)
y = np.linspace(-1.3, 1.3, N)
z = np.linspace(-1.3, 1.3, N)
x, y, z = np.meshgrid(x, y, z)
return (2 * x**2 + y**2 + z**2 -
1)**3 - (1 / 10) * x**2 * z**3 - y**2 * z**3 < 0
if __name__ == "__main__":
voxels = heart_voxel_grid(64)
m = Mesh.from_voxel_grid(voxels, colors=(0.8, 0, 0))
l = Lines.from_voxel_grid(voxels, colors=(0, 0, 0.), width=0.001)
show([l, m])
CameraTargetTrajectory(
BackAndForth(TrajectoryLine(
args.camera_target,
args.camera_target - np.array(args.up_vector)*5
)),
speed=0.001
),
CameraTrajectory(
BackAndForth(TrajectoryLine(
args.camera_position,
args.camera_position + d*15
)),
speed=0.001
)
]
if args.sorting:
behaviours.append(SortTriangles())
# Behaviours do be considered while rendering
if args.without_screen:
behaviours += [
SaveFrames(args.save_frames, 2),
SaveGif("/tmp/out.gif", 2)
]
render(renderables, size=args.window_size, behaviours=behaviours,
n_frames=args.n_frames)
else:
show(renderables, size=args.window_size,
behaviours=behaviours + [SnapshotOnKey()])
import numpy as np
import matplotlib.pyplot as plt
from simple_3dviz.renderables import Spherecloud
from simple_3dviz.behaviours.keyboard import SnapshotOnKey
from simple_3dviz.window import show
if __name__ == "__main__":
t = np.linspace(0, 4 * np.pi, 20)
x = np.sin(2 * t)
y = np.cos(t)
z = np.cos(2 * t)
sizes = (2 + np.sin(t)) * 0.125
centers = np.stack([x, y, z]).reshape(3, -1).T
cmap = plt.cm.copper
colors = cmap(np.random.choice(np.arange(500), centers.shape[0]))
s = Spherecloud(centers=centers, sizes=sizes, colors=colors)
from simple_3dviz import Mesh
m = Mesh.from_file("models/baby_yoda.stl", color=(0.1, 0.8, 0.1))
m.to_unit_cube()
show([s, m], camera_position=(-2.8, -2.8, 0.1), size=(512, 512))
from simple_3dviz.renderables import Mesh
from simple_3dviz.behaviours.keyboard import SnapshotOnKey
from simple_3dviz.window import show
if __name__ == "__main__":
# Number of e1, e2 parameters to be tested
N = 7
# SQs shapes
e2 = np.linspace(0.1, 1.9, N, endpoint=True)
e1 = np.linspace(0.1, 1.9, N, endpoint=True)
epsilon_1, epsilon_2 = np.meshgrid(e1, e2)
epsilons = np.stack([epsilon_1, epsilon_2]).reshape(2, -1).T
# SQs sizes
alphas = np.ones((epsilons.shape[0], 3))
# SQs translations
s = np.ceil(N*2.5 / 2)
x = np.linspace(-s, s, N)
y = np.linspace(-s, s, N)
z = np.array([0])
X, Y, Z = np.meshgrid(x, y, z)
translations = np.stack([X, Y, Z]).reshape(3, -1).T
# SQs rotations
rotations = np.eye(3)[np.newaxis] * np.ones((len(epsilons), 1, 1))
colors = np.array([[1., 0, 0, 1],
[0, 1, 1, 1]])[np.random.randint(0, 2, size=epsilons.shape[0])]
m = Mesh.from_superquadrics(alphas, epsilons, translations, rotations, colors)
show(m, size=(512,512), light=(0, 0, 3), behaviours=[SnapshotOnKey()])