def rotate(angle, axis):
rot = transforms.rotation_matrix(angle, axis, deg=True)
new_cam_xyz = transforms.apply44(rot, camera_xyz[None, :]).ravel()
new_up = transforms.apply44(rot, up[None, :]).ravel()
return transforms.lookat_matrix(cam_xyz=new_cam_xyz,
obj_xyz=object_xyz,
up=new_up)
def make_six_views(camera_xyz, object_xyz, up):
camera_xyz = np.array(camera_xyz).ravel().astype(np.float64)
object_xyz = np.array(object_xyz).ravel().astype(np.float64)
up = np.array(up).ravel().astype(np.float64)
viewing_dir = object_xyz - camera_xyz
viewing_dir /= la.norm(viewing_dir)
left = np.cross(up, viewing_dir)
# front
Rt_list = [
transforms.lookat_matrix(cam_xyz=camera_xyz, obj_xyz=object_xyz, up=up)
]
def rotate(angle, axis):
rot = transforms.rotation_matrix(angle, axis, deg=True)
new_cam_xyz = transforms.apply44(rot, camera_xyz[None, :]).ravel()
new_up = transforms.apply44(rot, up[None, :]).ravel()
return transforms.lookat_matrix(cam_xyz=new_cam_xyz,
obj_xyz=object_xyz,
up=new_up)
Rt_list.append(rotate(180, up)) # back
Rt_list.append(rotate(-90, up)) # left
Rt_list.append(rotate(90, up)) # right
Rt_list.append(rotate(90, left)) # top
# bottom
rot = transforms.rotation_matrix(90, left, deg=True)
rot2 = transforms.rotation_matrix(180, viewing_dir, deg=True)
new_cam_xyz = transforms.apply44(rot2.dot(rot),
camera_xyz[None, :]).ravel()
new_up = transforms.apply44(rot, up[None, :]).ravel()
Rt_list.append(
transforms.lookat_matrix(cam_xyz=new_cam_xyz,
obj_xyz=object_xyz,
up=new_up))
return Rt_list
def get_Rt_from_RenderForCNN_parameters(params):
"""
Computes a camera matrix that transforms models coordinates to blender's camera coordinates given RenderForCNN camera parameters.
Transformation steps in blender:
((model coordinates, blender import config) -> blender world coordinates, params) -> blender camera coordinates
This function:
(model coordinates, params) -> blender camera coordinates.
:param params: azimuth, elevation, tilt, distance from origin. All angles are in degrees.
:return: (3, 4) camera matrix.
"""
assert len(params) == 4
assert not isinstance(params[0], (np.ndarray, list, tuple))
# Same ordering used in RenderForCNN.
azimuth_deg, elevation_deg, theta_deg, rho = params
cam_xyz = transforms.sph_to_xyz([rho, 90 - elevation_deg, azimuth_deg],
is_input_radians=False)
# Blender's default obj import config:
# up (z): +y forward (y): -z
cam_xyz[1], cam_xyz[2] = cam_xyz[2], -cam_xyz[1]
# In model coordinates, default up vector is y+
# "lookat" position is assumed to be (0,0,0)
M0 = transforms.lookat_matrix(cam_xyz=cam_xyz,
obj_xyz=(0, 0, 0),
up=(0, 1, 0))
M = np.eye(4)
M[:3, :] = M0
M0 = M
T_tilt = transforms.rotation_matrix(-theta_deg,
direction=np.array((0, 0, 1)),
deg=True)
# Rt: move camera then tilt in camera space.
M = T_tilt.dot(M0)
Rt = M[:3]
assert Rt.shape == (3, 4)
assert Rt.dtype == np.float64
return Rt
def cam_Rt(self):
Rt = transforms.lookat_matrix(cam_xyz=self.cam_eye(),
obj_xyz=self.cam_lookat(),
up=self.cam_up())
return Rt
def main():
syn_images_dir = '/data/mvshape/shapenetcore/single_rgb_128/'
shapenetcore_dir = '/data/shapenetcore/ShapeNetCore.v1/'
log.info('Getting all filenames.')
syndirs = sorted(glob.glob(path.join(syn_images_dir, '*')))
filenames = []
for syndir in syndirs:
modeldirs = sorted(glob.glob(path.join(syndir, '*')))
if is_subset:
modeldirs = modeldirs[:10]
for modeldir in modeldirs:
renderings = sorted(glob.glob(path.join(modeldir, '*.png')))
if is_subset:
renderings = renderings[:7]
filenames.extend(renderings)
# random.seed(42)
# if not is_subset:
# random.shuffle(filenames)
# filenames = filenames[:1000000]
random.seed(42)
log.info('{} files'.format(len(filenames)))
# TODO
target_dir = '/data/mvshape/database'
if is_subset:
sqlite_file_path = join(target_dir, 'shapenetcore_subset.sqlite')
else:
sqlite_file_path = join(target_dir, 'shapenetcore.sqlite')
output_cam_distance_from_origin = 2
log.info('Setting up output directory.')
# set up debugging directory.
if path.isfile(sqlite_file_path):
os.remove(sqlite_file_path)
io_utils.ensure_dir_exists(target_dir)
# used for making sure there is no duplicate.
duplicate_name_check_set = set()
log.info('Checking for duplicates. And making sure params.txt exists.')
for i, filename in enumerate(filenames):
m = re.search(r'single_rgb_128/(.*?)/(.*?)/[^_]+?_[^_]+?_v(\d{4,})_a',
filename)
synset = m.group(1)
model_name = m.group(2)
v = m.group(3)
image_num = int(v)
vc_rendering_name = '{}_{}_{:04d}'.format(synset, model_name,
image_num)
if vc_rendering_name in duplicate_name_check_set:
print('duplicate found: ', (filename, vc_rendering_name))
duplicate_name_check_set.add(vc_rendering_name)
params_filename = join(syn_images_dir, synset, model_name,
'params.txt')
assert path.isfile(params_filename)
# Create the database
dbm.init(sqlite_file_path)
with dbm.db.transaction() as txn:
log.info('Creating common objects.')
make_dataset('shapenetcore')
make_rendering_type('rgb')
make_rendering_type('depth')
make_rendering_type('normal')
make_rendering_type('voxels')
make_tag('novelview')
make_tag('novelmodel')
make_tag('novelclass')
make_tag('perspective_input')
make_tag('orthographic_input')
make_tag('perspective_output')
make_tag('orthographic_output')
make_tag('viewer_centered')
make_tag('object_centered')
make_tag('real_world')
make_split('train')
make_split('test')
make_split('validation')
# Quote from http://shapenet.cs.stanford.edu/shapenet/obj-zip/ShapeNetCore.v1/README.txt
# "The OBJ files have been pre-aligned so that the up direction is the +Y axis, and the front is the +X axis. In addition each model is normalized to fit within a unit cube centered at the origin."
oc_output_cam = camera.OrthographicCamera.from_Rt(
transforms.lookat_matrix(cam_xyz=(0, 0,
output_cam_distance_from_origin),
obj_xyz=(0, 0, 0),
up=(0, 1, 0)),
wh=(128, 128),
is_world_to_cam=True)
db_oc_output_cam = get_db_camera(oc_output_cam, fov=None)
# Prepare all category objects.
log.info('Preparing categories.')
synset_db_category_map = {}
for synset, synset_name in synset_name_pairs:
db_category_i, _ = dbm.Category.get_or_create(name=synset_name)
synset_db_category_map[synset] = db_category_i
txn.commit()
# Prepare all mesh model objects.
# ---------------------------------------------
db_object_map = {}
# model_name -> {rendering_type_name -> rendering}
db_object_centered_renderings = {}
log.info('Preparing mesh model objects.')
start_time = time.time()
count = 0
for i, filename in enumerate(filenames):
m = re.search(
r'single_rgb_128/(.*?)/(.*?)/[^_]+?_[^_]+?_v(\d{4,})_a',
filename)
synset = m.group(1)
model_name = m.group(2)
if model_name not in db_object_map:
mesh_filename = join(shapenetcore_dir, synset, model_name,
'model.obj')
assert path.isfile(mesh_filename)
mesh_filename_suffix = join(
'/mesh/shapenetcore/v1',
'/'.join(mesh_filename.split('/')[-3:]))
db_category = synset_db_category_map[synset]
# Must be unique.
db_object = dbm.Object.create(
name=model_name,
category=db_category,
dataset=datasets['shapenetcore'],
num_vertices=0, # Not needed for now. Easy to fill in later.
num_faces=0,
mesh_filename=mesh_filename_suffix,
)
db_object_map[model_name] = db_object
oc_rendering_name = '{}_{}'.format(synset, model_name)
assert model_name not in db_object_centered_renderings
db_object_centered_renderings[model_name] = {
'output_rgb':
dbm.ObjectRendering.create(
type=rendering_types['rgb'],
camera=db_oc_output_cam,
object=db_object,
# JPG
filename='/shapenetcore/mv20_rgb_128/{}.bin'.format(
oc_rendering_name),
resolution=128,
num_channels=3,
set_size=20,
is_normalized=False,
),
'output_depth':
dbm.ObjectRendering.create(
type=rendering_types['depth'],
camera=db_oc_output_cam,
object=db_object,
# Since there is only one gt rendering per model, their id is the same as the model name.
filename='/shapenetcore/mv20_depth_128/{}.bin'.format(
oc_rendering_name),
resolution=128,
num_channels=1,
set_size=20,
is_normalized=False,
),
'output_normal':
dbm.ObjectRendering.create(
type=rendering_types['normal'],
camera=db_oc_output_cam,
object=db_object,
filename='/shapenetcore/mv20_normal_128/{}.bin'.format(
oc_rendering_name),
resolution=128,
num_channels=3,
set_size=20,
is_normalized=False,
),
'output_voxels':
dbm.ObjectRendering.create(
type=rendering_types['voxels'],
camera=db_oc_output_cam,
object=db_object,
filename='/shapenetcore/voxels_32/{}.bin'.format(
oc_rendering_name),
resolution=32,
num_channels=1,
set_size=1,
is_normalized=False,
)
}
if count % 5000 == 0:
txn.commit()
t_elapsed = (time.time() - start_time)
t_remaining = (t_elapsed / (i + 1) * (len(filenames) - i))
log.info(
'Creating mesh objects in db. {} of {}. elapsed: {:.1f} min, remaining: {:.1f} min'
.format(i, len(filenames), t_elapsed / 60,
t_remaining / 60))
count += 1
txn.commit()
t_elapsed = time.time() - start_time
log.info('created {} mesh objects in db. elapsed: {:.1f} min'.format(
count, t_elapsed / 60))
start_time = time.time()
log.info('Processing rgb images.')
for i, filename in enumerate(filenames):
m = re.search(
r'single_rgb_128/(.*?)/(.*?)/[^_]+?_[^_]+?_v(\d{4,})_a',
filename)
synset = m.group(1)
model_name = m.group(2)
v = m.group(3)
image_num = int(v)
params_filename = join(syn_images_dir, synset, model_name,
'params.txt')
assert path.isfile(params_filename)
lines = render_for_cnn_utils.read_params_file(params_filename)
Rt = render_for_cnn_utils.get_Rt_from_RenderForCNN_parameters(
lines[image_num])
# Input and output cameras
# -------------------
input_cam = camera.OrthographicCamera.from_Rt(Rt,
wh=(128, 128),
is_world_to_cam=True)
# 49.1343 degrees is the default fov in blender.
db_input_cam = get_db_camera(input_cam, fov=49.1343)
input_cam_depth_xyz = (input_cam.pos /
la.norm(input_cam.pos)) * 1.5
input_cam_depth_Rt = transforms.lookat_matrix(
cam_xyz=input_cam_depth_xyz,
obj_xyz=(0, 0, 0),
up=input_cam.up_vector)
input_cam_depth = camera.OrthographicCamera.from_Rt(
input_cam_depth_Rt, wh=(128, 128), is_world_to_cam=True)
db_input_cam_depth = get_db_camera(input_cam_depth, fov=49.1343)
output_cam_xyz = (input_cam.pos / la.norm(
input_cam.pos)) * output_cam_distance_from_origin
output_Rt = transforms.lookat_matrix(cam_xyz=output_cam_xyz,
obj_xyz=(0, 0, 0),
up=input_cam.up_vector)
vc_output_cam = camera.OrthographicCamera.from_Rt(
output_Rt, wh=(128, 128), is_world_to_cam=True)
db_vc_output_cam = get_db_camera(vc_output_cam, fov=None)
# ---
db_object = db_object_map[model_name]
vc_rendering_name = '{}_{}_{:04d}'.format(synset, model_name,
image_num)
# Viewer centered renderings.
# --------------------------------
# Input rgb image:
db_object_rendering_input_rgb = dbm.ObjectRendering.create(
type=rendering_types['rgb'],
camera=db_input_cam,
object=db_object,
# This should already exist.
filename='/shapenetcore/single_rgb_128/{}.png'.format(
vc_rendering_name),
resolution=128,
num_channels=1,
set_size=1,
is_normalized=False, # False for rgb.
)
db_object_rendering_input_depth = dbm.ObjectRendering.create(
type=rendering_types['depth'],
camera=db_input_cam_depth,
object=db_object,
filename='/shapenetcore/single_depth_128/{}.bin'.format(
vc_rendering_name),
resolution=128,
num_channels=1,
set_size=1,
is_normalized=True,
)
db_object_rendering_vc_output_rgb = dbm.ObjectRendering.create(
type=rendering_types['rgb'],
camera=db_vc_output_cam,
object=db_object,
filename='/shapenetcore/mv20_rgb_128/{}.bin'.format(
vc_rendering_name),
resolution=128,
num_channels=3,
set_size=20,
is_normalized=False,
)
db_object_rendering_vc_output_depth = dbm.ObjectRendering.create(
type=rendering_types['depth'],
camera=db_vc_output_cam,
object=db_object,
filename='/shapenetcore/mv20_depth_128/{}.bin'.format(
vc_rendering_name),
resolution=128,
num_channels=1,
set_size=20,
is_normalized=False,
)
db_object_rendering_vc_output_normal = dbm.ObjectRendering.create(
type=rendering_types['normal'],
camera=db_vc_output_cam,
object=db_object,
filename='/shapenetcore/mv20_normal_128/{}.bin'.format(
vc_rendering_name),
resolution=128,
num_channels=3,
set_size=20,
is_normalized=False,
)
db_object_rendering_vc_output_voxels = dbm.ObjectRendering.create(
type=rendering_types['voxels'],
camera=db_vc_output_cam,
object=db_object,
filename='/shapenetcore/voxels_32/{}.bin'.format(
vc_rendering_name),
resolution=32,
num_channels=1,
set_size=1,
is_normalized=False,
)
# Examples
# ----------------
# A row in the `Example` table is just an id for many-to-many references.
# View centered
example_viewer_centered = dbm.Example.create()
dbm.ExampleObjectRendering.create(
example=example_viewer_centered,
rendering=db_object_rendering_input_rgb)
dbm.ExampleObjectRendering.create(
example=example_viewer_centered,
rendering=db_object_rendering_input_depth)
dbm.ExampleObjectRendering.create(
example=example_viewer_centered,
rendering=db_object_rendering_vc_output_depth)
dbm.ExampleObjectRendering.create(
example=example_viewer_centered,
rendering=db_object_rendering_vc_output_normal)
dbm.ExampleObjectRendering.create(
example=example_viewer_centered,
rendering=db_object_rendering_vc_output_rgb)
dbm.ExampleObjectRendering.create(
example=example_viewer_centered,
rendering=db_object_rendering_vc_output_voxels)
dbm.ExampleDataset.create(example=example_viewer_centered,
dataset=datasets['shapenetcore'])
dbm.ExampleSplit.create(example=example_viewer_centered,
split=splits['train'])
dbm.ExampleTag.create(example=example_viewer_centered,
tag=tags['real_world'])
dbm.ExampleTag.create(example=example_viewer_centered,
tag=tags['viewer_centered'])
dbm.ExampleTag.create(example=example_viewer_centered,
tag=tags['perspective_input'])
dbm.ExampleTag.create(example=example_viewer_centered,
tag=tags['orthographic_output'])
dbm.ExampleTag.create(example=example_viewer_centered,
tag=tags['novelmodel'])
# Object centered
example_object_centered = dbm.Example.create()
dbm.ExampleObjectRendering.create(
example=example_object_centered,
rendering=db_object_rendering_input_rgb)
dbm.ExampleObjectRendering.create(
example=example_object_centered,
rendering=db_object_rendering_input_depth)
dbm.ExampleObjectRendering.create(
example=example_object_centered,
rendering=db_object_centered_renderings[model_name]
['output_depth'])
dbm.ExampleObjectRendering.create(
example=example_object_centered,
rendering=db_object_centered_renderings[model_name]
['output_normal'])
dbm.ExampleObjectRendering.create(
example=example_object_centered,
rendering=db_object_centered_renderings[model_name]
['output_rgb'])
dbm.ExampleObjectRendering.create(
example=example_object_centered,
rendering=db_object_centered_renderings[model_name]
['output_voxels'])
dbm.ExampleDataset.create(example=example_object_centered,
dataset=datasets['shapenetcore'])
dbm.ExampleSplit.create(example=example_object_centered,
split=splits['train'])
dbm.ExampleTag.create(example=example_object_centered,
tag=tags['real_world'])
dbm.ExampleTag.create(example=example_object_centered,
tag=tags['object_centered'])
dbm.ExampleTag.create(example=example_object_centered,
tag=tags['perspective_input'])
dbm.ExampleTag.create(example=example_object_centered,
tag=tags['orthographic_output'])
dbm.ExampleTag.create(example=example_object_centered,
tag=tags['novelmodel'])
if i % 5000 == 0:
txn.commit()
t_elapsed = (time.time() - start_time)
t_remaining = (t_elapsed / (i + 1) * (len(filenames) - i))
log.info(
'Creating examples in db. {} of {}. elapsed: {:.1f} min, remaining: {:.1f} min'
.format(i, len(filenames), t_elapsed / 60,
t_remaining / 60))
txn.commit()
dbm.db.commit()
t_elapsed = (time.time() - start_time)
log.info('total elapsed: {:.1f} min'.format(t_elapsed / 60))
def main():
with open('/data/mvshape/pascal3d/eval_1500.json', 'r') as f:
eval_1500 = json.load(f)
with open('/data/mvshape/pascal3d/validation_1200.json', 'r') as f:
validation_1200 = json.load(f)
validation_matfiles = []
for c, l in validation_1200.items():
for fname in l:
validation_matfiles.append(fname)
test_matfiles = []
for c, l in eval_1500.items():
for fname in l:
test_matfiles.append(fname)
database_filename = '/data/mvshape/database/pascal3d.sqlite'
if path.isfile(database_filename):
os.remove(database_filename)
dbm.init(database_filename)
with dbm.db.transaction() as txn:
make_dataset('pascal3d')
make_rendering_type('rgb')
make_rendering_type('depth')
make_rendering_type('voxels')
make_rendering_type('normal')
make_tag('novelview')
make_tag('novelmodel')
make_tag('novelclass')
make_tag('perspective_input')
make_tag('orthographic_input')
make_tag('perspective_output')
make_tag('orthographic_output')
make_tag('viewer_centered')
make_tag('object_centered')
make_tag('real_world')
make_split('test')
make_split('validation')
# Quote from http://shapenet.cs.stanford.edu/shapenet/obj-zip/ShapeNetCore.v1/README.txt
# "The OBJ files have been pre-aligned so that the up direction is the +Y axis, and the front is the +X axis. In addition each model is normalized to fit within a unit cube centered at the origin."
oc_output_cam = camera.OrthographicCamera.from_Rt(transforms.lookat_matrix(cam_xyz=(0, 0, output_cam_distance_from_origin),
obj_xyz=(0, 0, 0),
up=(0, 1, 0)),
wh=(128, 128),
is_world_to_cam=True)
db_oc_output_cam = get_db_camera(oc_output_cam, fov=None)
txn.commit()
all_matfiles = test_matfiles + validation_matfiles
# populate categories
for matfile in all_matfiles:
anno = load_annotation_object(matfile)
if anno.category_name in categories:
continue
db_category_i, _ = dbm.Category.get_or_create(name=anno.category_name)
categories[anno.category_name] = db_category_i
for matfile in all_matfiles:
anno = load_annotation_object(matfile)
model_name = '{}_{:02d}'.format(anno.category_name, anno.cad_index)
if model_name in db_object_map:
continue
new_mesh_filename_db = '/mesh/pascal3d/{}.off'.format(model_name)
new_mesh_filename = make_fullpath(new_mesh_filename_db)
if not path.isfile(new_mesh_filename):
io_utils.save_off(anno.fv(), new_mesh_filename)
db_object = dbm.Object.create(
name=model_name,
category=categories[anno.category_name],
dataset=datasets['pascal3d'],
num_vertices=0, # Not needed for now. Easy to fill in later.
num_faces=0,
mesh_filename=new_mesh_filename_db,
)
db_object_map[model_name] = db_object
oc_rendering_name = model_name
assert model_name not in db_object_centered_renderings
db_object_centered_renderings[model_name] = {
'output_rgb': dbm.ObjectRendering.create(
type=rendering_types['rgb'],
camera=db_oc_output_cam,
object=db_object,
# JPG
filename='/pascal3d/mv6_rgb_128/{}.bin'.format(oc_rendering_name),
resolution=128,
num_channels=3,
set_size=6,
is_normalized=False,
),
'output_depth': dbm.ObjectRendering.create(
type=rendering_types['depth'],
camera=db_oc_output_cam,
object=db_object,
# Since there is only one gt rendering per model, their id is the same as the model name.
filename='/pascal3d/mv6_depth_128/{}.bin'.format(oc_rendering_name),
resolution=128,
num_channels=1,
set_size=6,
is_normalized=False,
),
'output_normal': dbm.ObjectRendering.create(
type=rendering_types['normal'],
camera=db_oc_output_cam,
object=db_object,
filename='/pascal3d/mv6_normal_128/{}.bin'.format(oc_rendering_name),
resolution=128,
num_channels=3,
set_size=6,
is_normalized=False,
),
'output_voxels': dbm.ObjectRendering.create(
type=rendering_types['voxels'],
camera=db_oc_output_cam,
object=db_object,
filename='/pascal3d/voxels_32/{}.bin'.format(oc_rendering_name),
resolution=32,
num_channels=1,
set_size=1,
is_normalized=False,
)
}
txn.commit()
for matfile in all_matfiles:
anno = load_annotation_object(matfile) # type: pascal.PascalAnnotation
model_name = '{}_{:02d}'.format(anno.category_name, anno.cad_index)
input_cam = anno.cam_object()
# 49.1343 degrees is the default fov in blender.
db_input_cam = get_db_camera(input_cam, fov=49.1343)
input_cam_depth_xyz = (input_cam.pos / la.norm(input_cam.pos)) * 1.5
input_cam_depth_Rt = transforms.lookat_matrix(cam_xyz=input_cam_depth_xyz,
obj_xyz=(0, 0, 0),
up=input_cam.up_vector)
input_cam_depth = camera.OrthographicCamera.from_Rt(input_cam_depth_Rt, wh=(128, 128), is_world_to_cam=True)
db_input_cam_depth = get_db_camera(input_cam_depth, fov=49.1343)
output_cam_xyz = (input_cam.pos / la.norm(input_cam.pos)) * output_cam_distance_from_origin
output_Rt = transforms.lookat_matrix(cam_xyz=output_cam_xyz,
obj_xyz=(0, 0, 0),
up=input_cam.up_vector)
vc_output_cam = camera.OrthographicCamera.from_Rt(output_Rt, wh=(128, 128), is_world_to_cam=True)
db_vc_output_cam = get_db_camera(vc_output_cam, fov=None)
# ---
db_object = db_object_map[model_name]
# e.g. aeroplane_01_n03693474_7232
vc_rendering_name = '{}_{}'.format(model_name, anno.jpg_filename.split('.')[0])
# Input rgb image:
db_object_rendering_input_rgb = dbm.ObjectRendering.create(
type=rendering_types['rgb'],
camera=db_input_cam,
object=db_object,
# This should already exist.
filename='/pascal3d/single_rgb_128/{}.png'.format(vc_rendering_name),
resolution=128,
num_channels=1,
set_size=1,
is_normalized=False, # False for rgb.
)
db_object_rendering_input_depth = dbm.ObjectRendering.create(
type=rendering_types['depth'],
camera=db_input_cam_depth,
object=db_object,
filename='/pascal3d/single_depth_128/{}.bin'.format(vc_rendering_name),
resolution=128,
num_channels=1,
set_size=1,
is_normalized=True,
)
db_object_rendering_vc_output_rgb = dbm.ObjectRendering.create(
type=rendering_types['rgb'],
camera=db_vc_output_cam,
object=db_object,
filename='/pascal3d/mv6_rgb_128/{}.bin'.format(vc_rendering_name),
resolution=128,
num_channels=3,
set_size=6,
is_normalized=False,
)
db_object_rendering_vc_output_depth = dbm.ObjectRendering.create(
type=rendering_types['depth'],
camera=db_vc_output_cam,
object=db_object,
filename='/pascal3d/mv6_depth_128/{}.bin'.format(vc_rendering_name),
resolution=128,
num_channels=1,
set_size=6,
is_normalized=False,
)
db_object_rendering_vc_output_normal = dbm.ObjectRendering.create(
type=rendering_types['normal'],
camera=db_vc_output_cam,
object=db_object,
filename='/pascal3d/mv6_normal_128/{}.bin'.format(vc_rendering_name),
resolution=128,
num_channels=3,
set_size=6,
is_normalized=False,
)
db_object_rendering_vc_output_voxels = dbm.ObjectRendering.create(
type=rendering_types['voxels'],
camera=db_vc_output_cam,
object=db_object,
filename='/pascal3d/voxels_32/{}.bin'.format(vc_rendering_name),
resolution=32,
num_channels=1,
set_size=1,
is_normalized=False,
)
# Examples
# ----------------
is_test = matfile in test_matfiles
is_validation = matfile in validation_matfiles
assert is_test ^ is_validation
if is_test:
split = splits['test']
elif is_validation:
split = splits['validation']
else:
raise NotImplementedError()
# A row in the `Example` table is just an id for many-to-many references.
# View centered
example_viewer_centered = dbm.Example.create()
dbm.ExampleObjectRendering.create(example=example_viewer_centered, rendering=db_object_rendering_input_rgb)
dbm.ExampleObjectRendering.create(example=example_viewer_centered, rendering=db_object_rendering_input_depth)
dbm.ExampleObjectRendering.create(example=example_viewer_centered, rendering=db_object_rendering_vc_output_depth)
dbm.ExampleObjectRendering.create(example=example_viewer_centered, rendering=db_object_rendering_vc_output_normal)
dbm.ExampleObjectRendering.create(example=example_viewer_centered, rendering=db_object_rendering_vc_output_rgb)
dbm.ExampleObjectRendering.create(example=example_viewer_centered, rendering=db_object_rendering_vc_output_voxels)
dbm.ExampleDataset.create(example=example_viewer_centered, dataset=datasets['pascal3d'])
dbm.ExampleSplit.create(example=example_viewer_centered, split=split)
dbm.ExampleTag.create(example=example_viewer_centered, tag=tags['real_world'])
dbm.ExampleTag.create(example=example_viewer_centered, tag=tags['viewer_centered'])
dbm.ExampleTag.create(example=example_viewer_centered, tag=tags['perspective_input'])
dbm.ExampleTag.create(example=example_viewer_centered, tag=tags['orthographic_output'])
dbm.ExampleTag.create(example=example_viewer_centered, tag=tags['novelmodel'])
# Object centered
example_object_centered = dbm.Example.create()
dbm.ExampleObjectRendering.create(example=example_object_centered, rendering=db_object_rendering_input_rgb)
dbm.ExampleObjectRendering.create(example=example_object_centered, rendering=db_object_rendering_input_depth)
dbm.ExampleObjectRendering.create(example=example_object_centered, rendering=db_object_centered_renderings[model_name]['output_depth'])
dbm.ExampleObjectRendering.create(example=example_object_centered, rendering=db_object_centered_renderings[model_name]['output_normal'])
dbm.ExampleObjectRendering.create(example=example_object_centered, rendering=db_object_centered_renderings[model_name]['output_rgb'])
dbm.ExampleObjectRendering.create(example=example_object_centered, rendering=db_object_centered_renderings[model_name]['output_voxels'])
dbm.ExampleDataset.create(example=example_object_centered, dataset=datasets['pascal3d'])
dbm.ExampleSplit.create(example=example_object_centered, split=split)
dbm.ExampleTag.create(example=example_object_centered, tag=tags['real_world'])
dbm.ExampleTag.create(example=example_object_centered, tag=tags['object_centered'])
dbm.ExampleTag.create(example=example_object_centered, tag=tags['perspective_input'])
dbm.ExampleTag.create(example=example_object_centered, tag=tags['orthographic_output'])
dbm.ExampleTag.create(example=example_object_centered, tag=tags['novelmodel'])
txn.commit()
dbm.db.commit()