def vis(*dense_data):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
for d in dense_data:
mlab.figure()
vis_voxels(d, color=(0, 0, 1), axis_order='xyz')
mlab.show()
def vis_voxels(model_id, edge_length_threshold, filled, shuffle=False):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
from shapenet.core import cat_desc_to_id
from template_ffd.inference.voxels import get_voxel_dataset
from template_ffd.data.voxels import get_gt_voxel_dataset
from template_ffd.model import load_params
from template_ffd.data.ids import get_example_ids
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
gt_ds = get_gt_voxel_dataset(cat_id, filled)
inf_ds = get_voxel_dataset(model_id, edge_length_threshold)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
example_ids.shuffle
with gt_ds:
with inf_ds:
for example_id in example_ids:
gt = gt_ds[example_id].data
inf = inf_ds[example_id].data
vis_voxels(gt, color=(0, 0, 1))
mlab.figure()
vis_voxels(inf, color=(0, 1, 0))
mlab.show()
def vis_voxels(model_id, edge_length_threshold, filled, shuffle=False):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
from shapenet.core import cat_desc_to_id
from template_ffd.inference.voxels import get_voxel_dataset
from template_ffd.data.voxels import get_gt_voxel_dataset
from template_ffd.model import load_params
from template_ffd.data.ids import get_example_ids
cat_id = cat_desc_to_id(load_params(model_id)['cat_desc'])
gt_ds = get_gt_voxel_dataset(cat_id, filled)
inf_ds = get_voxel_dataset(model_id, edge_length_threshold)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
example_ids.shuffle
with gt_ds:
with inf_ds:
for example_id in example_ids:
gt = gt_ds[example_id].data
inf = inf_ds[example_id].data
vis_voxels(gt, color=(0, 0, 1))
mlab.figure()
vis_voxels(inf, color=(0, 1, 0))
mlab.show()
def vis(vox, image, meta):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
print(meta)
image.resize((137 * 4, ) * 2).show()
mlab.figure()
vis_voxels(vox, color=(0, 0, 1), scale_factor=0.5, axis_order='xyz')
mlab.show()
def vis_voxels():
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
path = get_voxel_path('eval', 9)
voxels = load_mat_data(path)
vis_voxels(voxels, color=(0, 0, 1))
mlab.show()
def vis(base, frust, image):
from util3d.mayavi_vis import vis_voxels
from mayavi import mlab
from PIL import Image
frust_data = frust.dense_data()
frust_flat = np.max(frust_data, axis=-1)
Image.fromarray(frust_flat.T.astype(np.uint8) * 255).resize(
(224, 224)).show()
image.show()
mlab.figure()
vis_voxels(base.dense_data(), color=(0, 0, 1))
mlab.figure()
vis_voxels(frust_data, color=(0, 1, 0))
mlab.show()
def vis(image_fp, rle_data, out_dim):
import numpy as np
from PIL import Image
from util3d.voxel.binvox import RleVoxels
from util3d.mayavi_vis import vis_voxels
from mayavi import mlab
im = Image.open(image_fp)
im.show()
dense_data = RleVoxels(np.array(rle_data), (out_dim, ) * 3).dense_data()
sil = np.any(dense_data, axis=-1).T.astype(np.uint8) * 255
sil = Image.fromarray(sil).resize(im.size)
sil.show()
comb = np.array(im) // 2 + np.array(sil)[:, :, np.newaxis] // 2
Image.fromarray(comb).show()
vis_voxels(dense_data, color=(0, 0, 1))
mlab.show()
def vis(cat_desc, regime='e', shuffle=True):
import matplotlib.pyplot as plt
from mayavi import mlab
from util3d.mayavi_vis import vis_point_cloud, vis_voxels
all_ds = get_ds(cat_desc, regime)
cat_id = cat_desc_to_id(cat_desc)
example_ids = list(get_example_ids(cat_id, 'eval'))
random.shuffle(example_ids)
def vis_mesh(mesh, include_wireframe=False, **kwargs):
from util3d.mayavi_vis import vis_mesh as vm
v, f = (np.array(mesh[k]) for k in ('vertices', 'faces'))
vm(v, f, include_wireframe=include_wireframe, **kwargs)
with all_ds:
for example_id in example_ids:
print(example_id)
image, gt_mesh, cloud, mesh, voxels, template_mesh = \
all_ds[example_id]
plt.imshow(image)
mlab.figure()
vis_mesh(gt_mesh, color=(0, 0, 1))
mlab.figure()
vis_mesh(mesh, color=(0, 1, 0))
mlab.figure()
vis_mesh(template_mesh, color=(1, 0, 0))
mlab.figure()
vis_point_cloud(np.array(cloud),
scale_factor=0.01,
color=(0, 1, 0))
mlab.figure()
vis_voxels(voxels.data, color=(0, 1, 0))
plt.show(block=False)
mlab.show()
plt.close()
def vis(cat_desc, regime='e', shuffle=True):
import matplotlib.pyplot as plt
from mayavi import mlab
from util3d.mayavi_vis import vis_point_cloud, vis_voxels
all_ds = get_ds(cat_desc, regime)
cat_id = cat_desc_to_id(cat_desc)
example_ids = list(get_example_ids(cat_id, 'eval'))
random.shuffle(example_ids)
def vis_mesh(mesh, include_wireframe=False, **kwargs):
from util3d.mayavi_vis import vis_mesh as vm
v, f = (np.array(mesh[k]) for k in ('vertices', 'faces'))
vm(v, f, include_wireframe=include_wireframe, **kwargs)
with all_ds:
for example_id in example_ids:
print(example_id)
image, gt_mesh, cloud, mesh, voxels, template_mesh = \
all_ds[example_id]
plt.imshow(image)
mlab.figure()
vis_mesh(gt_mesh, color=(0, 0, 1))
mlab.figure()
vis_mesh(mesh, color=(0, 1, 0))
mlab.figure()
vis_mesh(template_mesh, color=(1, 0, 0))
mlab.figure()
vis_point_cloud(
np.array(cloud), scale_factor=0.01, color=(0, 1, 0))
mlab.figure()
vis_voxels(voxels.data, color=(0, 1, 0))
plt.show(block=False)
mlab.show()
plt.close()
def test_mpl_vis():
print(voxels.shape)
v = fast_resize(voxels, 32)
print(v.shape)
vis_voxels(v, color=(0, 0, 1), axis_order='xyz')
mlab.show()
def test_fast_resize():
# zoomed = resize(voxels, 64)
vis_voxels(fast_resize(voxels, 32), color=(0, 0, 1), axis_order='xyz')
mlab.show()
import os
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
from util3d.mesh.off import OffObject
from util3d.mesh.geom import triangulated_faces
from util3d.voxel.convert import mesh_to_voxels
from util3d.voxel.binvox import Voxels
folder = os.path.realpath(os.path.dirname(__file__))
vox_path = os.path.join(folder, 'data', 'bunny.binvox')
if not os.path.isfile(vox_path):
bunny_path = os.path.join(folder, 'data', 'bunny.off')
bunny = OffObject.from_path(bunny_path)
vertices = bunny.vertices
faces = tuple(triangulated_faces(bunny.faces))
voxels = mesh_to_voxels(vertices, faces, 64)
with open(os.path.join(folder, 'data', 'bunny.binvox'), 'w') as fp:
voxels.save_to_file(fp)
del voxels
with open(vox_path, 'r') as fp:
voxels = Voxels.from_file(fp)
vis_voxels(voxels.dense_data())
mlab.show()
inf_ds = get_voxel_dataset(model_id, edge_length_threshold)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
example_ids.shuffle
with gt_ds:
with inf_ds:
for example_id in example_ids:
gt = gt_ds[example_id].data
inf = inf_ds[example_id].data
vis_voxels(gt, color=(0, 0, 1))
mlab.figure()
vis_voxels(inf, color=(0, 1, 0))
mlab.show()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('model_id', help='id of model defined in params')
parser.add_argument('-t',
'--edge_length_threshold',
type=float,
default=0.1)
parser.add_argument('-s', '--shuffle', action='store_true')
parser.add_argument('-f', '--filled', action='store_true')
args = parser.parse_args()
vis_voxels(args.model_id, args.edge_length_threshold, args.filled,
args.shuffle)
def vis(voxels):
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
vis_voxels(voxels)
mlab.show()
from mayavi import mlab
from shapenet.core.voxels.config import VoxelConfig
from shapenet.core import cat_desc_to_id, get_example_ids
from util3d.mayavi_vis import vis_voxels
cat_desc = 'plane'
cat_id = cat_desc_to_id(cat_desc)
example_ids = get_example_ids(cat_id)
config = VoxelConfig()
with config.get_dataset(cat_id) as dataset:
for example_id in example_ids:
voxels = dataset[example_id]
vis_voxels(voxels.dense_data(), color=(0, 0, 1))
mlab.show()
gt_ds = get_gt_voxel_dataset(cat_id, filled)
inf_ds = get_voxel_dataset(model_id, edge_length_threshold)
example_ids = get_example_ids(cat_id, 'eval')
if shuffle:
example_ids = list(example_ids)
example_ids.shuffle
with gt_ds:
with inf_ds:
for example_id in example_ids:
gt = gt_ds[example_id].data
inf = inf_ds[example_id].data
vis_voxels(gt, color=(0, 0, 1))
mlab.figure()
vis_voxels(inf, color=(0, 1, 0))
mlab.show()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
'model_id', help='id of model defined in params')
parser.add_argument(
'-t', '--edge_length_threshold', type=float, default=0.1)
parser.add_argument('-s', '--shuffle', action='store_true')
parser.add_argument('-f', '--filled', action='store_true')
args = parser.parse_args()
vis_voxels(
args.model_id, args.edge_length_threshold, args.filled, args.shuffle)
return load_mat_data(get_voxel_path(mode, example_id))
if __name__ == '__main__':
from path import get_image_path
mode = 'eval'
example_id = 9
def vis_image():
import matplotlib.pyplot as plt
from scipy.misc import imread
image_idx = 0
image = imread(get_image_path(mode, example_id, image_idx))
image = image[..., :3]
plt.imshow(image)
plt.figure()
plt.imshow(image[..., -1])
plt.show()
def vis_voxels():
from mayavi import mlab
from util3d.mayavi_vis import vis_voxels
path = get_voxel_path('eval', 9)
voxels = load_mat_data(path)
vis_voxels(voxels, color=(0, 0, 1))
mlab.show()
vis_image()
vis_voxels()
dim = 32
with get_zipped_voxel_dataset(dataset_id, mode, category, dim) as dataset:
for example_id in dataset:
voxels = dataset[example_id]
dense = voxels.dense_data()
sparse = voxels.sparse_data()
contiguous = get_contiguous_regions_2d(voxels.rle_data(), dim)
reconstructed = np.zeros((dim,)*3, dtype=np.bool)
for n, c in enumerate(contiguous):
i = n // dim
j = n % dim
for a, b in c:
reconstructed[i, j, a:b] = True
print(np.all(dense == reconstructed))
mlab.figure()
vis_voxels(dense, color=(0, 0, 1))
mlab.figure()
vis_voxels(reconstructed, color=(0, 1, 0))
mlab.show()
# print(len(sparse))
# i = sparse[0][10]
# j = sparse[1][10]
# data = np.array(dense[i, j], dtype=np.float32)*2 - 0.5
# lower, frac = get_interpolated_roots_1d(data)
# print(data)
# print(lower)
# print(frac)
# exit()
folder = os.path.realpath(os.path.dirname(__file__))
dim = 128
vox_path = os.path.join(folder, 'data', 'bunny%d.binvox' % dim)
if not os.path.isfile(vox_path):
bunny_path = os.path.join(folder, 'data', 'bunny.off')
bunny = OffObject.from_path(bunny_path)
vertices = bunny.vertices
faces = tuple(triangulated_faces(bunny.faces))
voxels = mesh_to_voxels(vertices, faces, dim)
with open(vox_path, 'w') as fp:
voxels.save_to_file(fp)
del voxels
with open(vox_path, 'r') as fp:
voxels = Voxels.from_file(fp)
dense = voxels.dense_data()
filled = np.array(orthographic_filled_voxels(dense))
filled_v0 = v0(dense)
print(np.sum(dense), np.sum(filled), np.all(filled_v0 == filled))
mlab.figure()
# vis_contours(dense, contours=[0.5], color=(0, 0, 1))
vis_voxels(dense, color=(0, 0, 1), axis_order='xyz')
mlab.figure()
vis_voxels(filled, color=(0, 1, 0), axis_order='xyz')
mlab.show()