def write_projection_render(pfp, v, f, map):
CAM2WORLD = np.array([[0.85408425, 0.31617427, -0.375678, 0.56351697 * 2],
[0., -0.72227067, -0.60786998, 0.91180497 * 2],
[-0.52013469, 0.51917219, -0.61688, 0.92532003 * 2],
[0., 0., 0., 1.]],
dtype=np.float32)
# rotate the mesh elevation by 30 degrees
Rx = np.array(
[[1, 0, 0, 0], [0., np.cos(np.pi / 6), -np.sin(np.pi / 6), 0],
[0, np.sin(np.pi / 6), np.cos(np.pi / 6), 0], [0., 0., 0., 1.]],
dtype=np.float32)
CAM2WORLD = np.matmul(Rx, CAM2WORLD)
RENDER_INFO = {
'Height': 480,
'Width': 640,
'fx': 575,
'fy': 575,
'cx': 319.5,
'cy': 239.5
}
# TODO ---------- Render Stub -----------#
context = render.SetMesh(v, f)
for i_ang, _ in enumerate(np.linspace(0, 2 * np.pi, map.num_angs)):
render.render(context, map.world2cam_mats[i_ang])
# depth = render.getDepth(RENDER_INFO)
vindices, _, _ = render.getVMap(context, RENDER_INFO)
mask = np.unique(vindices)
# Only kept the mask option
np.savez(f"{pfp}_mask_{i_ang:03d}", mask=mask)
def voxelized_pointcloud_sampling(path):
try:
out_file = path + '/voxelized_point_cloud_{}res_{}points.npz'.format(
args.res, args.num_points)
if os.path.exists(out_file):
if args.write_over:
print('overwrite ', out_file)
else:
print('File exists. Done.')
return
off_path = path + '/isosurf_scaled.off'
# mesh = trimesh.load(off_path)
# point_cloud = mesh.sample(args.num_points)
V, F = objloader.LoadOff(off_path)
# set up camera information
info = {
'Height': 480,
'Width': 640,
'fx': 575,
'fy': 575,
'cx': 319.5,
'cy': 239.5
}
render.setup(info)
# set up mesh buffers in cuda
context = render.SetMesh(V, F)
# cam2world = np.array([[ 0.85408425, 0.31617427, -0.375678 , 0.56351697 * 2],
# [ 0. , -0.72227067, -0.60786998, 0.91180497 * 2],
# [-0.52013469, 0.51917219, -0.61688 , 0.92532003 * 2+3],
# [ 0. , 0. , 0. , 1. ]], dtype=np.float32)
cam2world = np.array(
[[0.85408425, 0.31617427, -0.375678, 0.56351697 * 2],
[0., -0.72227067, -0.60786998, 0.91180497 * 2],
[-0.52013469, 0.51917219, -0.61688, 0.92532003 * 2],
[0., 0., 0., 1.]],
dtype=np.float32)
world2cam = np.linalg.inv(cam2world).astype('float32')
# the actual rendering process
render.render(context, world2cam)
# get information of mesh rendering
# vindices represents 3 vertices related to pixels
vindices, vweights, findices = render.getVMap(context, info)
visible_indices = np.unique(vindices)
# visible_points = V[visible_indices]
# visible_faces = F[np.unique(findices)]
num_visible = visible_indices.shape[0]
# # faces for partial boun
# for i in range(num_visible):
# origin_index = visible_indices[i]
# visible_faces = np.where(visible_faces==origin_index,i,visible_faces)
# max_index = num_visible - 1
# faces_valid = np.ones((visible_faces.shape[0]),dtype=bool)
# for i in range(visible_faces.shape[0]):
# face = visible_faces[i]
# if face[0] > max_index or face[1] > max_index or face[2] > max_index:
# faces_valid[i] = False
# visible_faces = visible_faces[faces_valid]
# for visualization
# vis_face = findices.astype('float32') / np.max(findices)
# sio.imsave(path + '/face.png',vis_face)
# sio.imsave(path + '/vertex.png',vweights)
# return num_visible
if num_visible < args.num_points:
print("visible_num is ", num_visible)
raise Exception
sample_idx = np.random.choice(num_visible,
args.num_points,
replace=False)
valid_indices = visible_indices[sample_idx]
point_cloud = V[valid_indices]
# num_valid = point_cloud.shape[0]
# for i in range(num_valid):
# origin_index = valid_indices[i]
# visible_faces = np.where(visible_faces==origin_index,i,visible_faces)
# max_index = num_valid - 1
# faces_valid = np.ones((visible_faces.shape[0]),dtype=bool)
# for i in range(visible_faces.shape[0]):
# face = visible_faces[i]
# if face[0] > max_index or face[1] > max_index or face[2] > max_index:
# faces_valid[i] = False
# visible_faces = visible_faces[faces_valid]
occupancies = np.zeros(len(grid_points), dtype=np.int8)
_, idx = kdtree.query(point_cloud)
occupancies[idx] = 1
compressed_occupancies = np.packbits(occupancies)
np.savez(out_file,
point_cloud=point_cloud,
compressed_occupancies=compressed_occupancies,
bb_min=bb_min,
bb_max=bb_max,
res=args.res)
print('Finished {}'.format(path))
# prepare data for boundary sampling
# visible_
# visible_mesh = trimesh.base.Trimesh(vertices=point_cloud,faces=visible_faces)
# visible_mesh.export(path + '/vis_mesh.obj')
# boundary_sampling(path,visible_mesh)
except Exception as err:
print('Error with {}: {}'.format(path, traceback.format_exc()))
import sys
import skimage.io as sio
import os
#from gen_poses import GetPoses,WavePose
import shutil
from objloader import LoadTextureOBJ
import render
import objloader
input_obj = sys.argv[1]
V, F, VT, FT, VN, FN, face_mat, kdmap = objloader.LoadTextureOBJ(input_obj)
# set up camera information
info = {'Height':960, 'Width':1280, 'fx':575*2, 'fy':575*2, 'cx':640, 'cy':480}
render.setup(info)
context = render.SetMesh(V, F)
cam2world = np.array([[ 0.85408425, 0.31617427, -0.375678 , 0.56351697 * 2],
[ 0. , -0.72227067, -0.60786998, 0.91180497 * 2],
[-0.52013469, 0.51917219, -0.61688 , 0.92532003 * 2],
[ 0. , 0. , 0. , 1. ]], dtype=np.float32)
world2cam = np.linalg.inv(cam2world).astype('float32')
render.render(context, world2cam)
depth = render.getDepth(info)
vindices, vweights, findices = render.getVMap(context, info)
sio.imsave('depth.png', depth / np.max(depth))
sio.imsave('vweights.png', vweights)