def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.scene_ids is not None and FLAGS.targets_filename is not None:
raise ValueError(
'Only up to one of scene_ids and targets_filename can be specified.')
# Load dataset parameters.
dp_split = dataset_params.get_split_params(
config.BOP_PATH, FLAGS.dataset, FLAGS.split, FLAGS.split_type)
output_suffix = None
if FLAGS.targets_filename:
output_suffix = 'targets'
test_targets = inout.load_json(
os.path.join(config.BOP_PATH, FLAGS.dataset, FLAGS.targets_filename))
example_list = []
for trg in test_targets:
example = {'scene_id': trg['scene_id'], 'im_id': trg['im_id']}
if example not in example_list:
example_list.append(example)
else:
if FLAGS.scene_ids is None:
FLAGS.scene_ids = dataset_params.get_present_scene_ids(dp_split)
else:
FLAGS.scene_ids = list(map(int, FLAGS.scene_ids))
output_suffix = 'scenes-' + '-'.join(
map(lambda x: '{:01d}'.format(x), FLAGS.scene_ids))
tf.logging.info('Collecting examples...')
example_list = []
for scene_id in FLAGS.scene_ids:
scene_gt_fpath = dp_split['scene_gt_tpath'].format(scene_id=scene_id)
im_ids = inout.load_scene_gt(scene_gt_fpath).keys()
for im_id in sorted(im_ids):
example_list.append({'scene_id': scene_id, 'im_id': im_id})
tf.logging.info('Collected {} examples.'.format(len(example_list)))
assert(len(example_list) > 0)
split_name = FLAGS.split
if FLAGS.split_type is not None:
split_name += '-' + FLAGS.split_type
if output_suffix is not None:
output_suffix = '_' + output_suffix
else:
output_suffix = ''
output_fname = '{}_{}{}_examples.txt'.format(
FLAGS.dataset, split_name, output_suffix)
output_fpath = os.path.join(FLAGS.output_dir, output_fname)
tf.logging.info('Saving the list to: {}'.format(output_fpath))
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
tfrecord.save_example_list(output_fpath, example_list)
def __init__(self, name):
"""Constructor.
Args:
name: Dataset name. E.g., 's0_test'.
"""
self._name = name
self._dataset = get_dataset(self._name)
self._setup = self._name.split('_')[0]
self._split = self._name.split('_')[1]
self._out_dir = os.path.join(os.path.dirname(__file__), "..",
"results")
self._bop_dir = os.path.join(self._dataset.data_dir, "bop")
self._p = {
'errors': [
{
'n_top': -1,
'type': 'vsd',
'vsd_delta': 15,
'vsd_taus': list(np.arange(0.05, 0.51, 0.05)),
'correct_th': [[th] for th in np.arange(0.05, 0.51, 0.05)]
},
{
'n_top': -1,
'type': 'mssd',
'correct_th': [[th] for th in np.arange(0.05, 0.51, 0.05)]
},
{
'n_top': -1,
'type': 'mspd',
'correct_th': [[th] for th in np.arange(5, 51, 5)]
},
],
'visib_gt_min':
-1,
}
dp_split = dataset_params.get_split_params(self._bop_dir, self._setup,
self._split)
dp_model = dataset_params.get_model_params(self._bop_dir,
self._setup,
model_type='eval')
self._scene_ids = dp_split['scene_ids']
self._obj_ids = dp_model['obj_ids']
self._grasp_id = defaultdict(lambda: {})
for i in range(len(self._dataset)):
sample = self._dataset[i]
scene_id, im_id = self._dataset.get_bop_id_from_idx(i)
obj_id = sample['ycb_ids'][sample['ycb_grasp_ind']]
self._grasp_id[scene_id][im_id] = obj_id
ests_counter = 0
time_start = time.time()
# Parse info about the method and the dataset from the filename.
result_name = os.path.splitext(os.path.basename(result_filename))[0]
result_info = result_name.split('_')
method = str(result_info[0])
dataset_info = result_info[1].split('-')
dataset = str(dataset_info[0])
split = str(dataset_info[1])
split_type = str(dataset_info[2]) if len(dataset_info) > 2 else None
split_type_str = ' - ' + split_type if split_type is not None else ''
# Load dataset parameters.
dp_split = dataset_params.get_split_params(p['datasets_path'], dataset,
split, split_type)
model_type = 'eval'
dp_model = dataset_params.get_model_params(p['datasets_path'], dataset,
model_type)
# Load object models.
models = {}
if p['error_type'] in ['ad', 'add', 'adi', 'mssd', 'mspd', 'proj']:
misc.log('Loading object models...')
for obj_id in dp_model['obj_ids']:
models[obj_id] = inout.load_ply(
dp_model['model_tpath'].format(obj_id=obj_id))
# Load models info.
models_info = None
'dataset': 'lm',
# Dataset split. Options: 'train', 'val', 'test'.
'dataset_split': 'test',
# Dataset split type. None = default. See dataset_params.py for options.
'dataset_split_type': None,
# Folder containing the BOP datasets.
'datasets_path': config.datasets_path,
}
################################################################################
# Load dataset parameters.
dp_split = dataset_params.get_split_params(p['datasets_path'], p['dataset'],
p['dataset_split'],
p['dataset_split_type'])
scene_ids = dp_split['scene_ids']
dists = []
azimuths = []
elevs = []
visib_fracts = []
ims_count = 0
for scene_id in scene_ids:
misc.log('Processing - dataset: {} ({}, {}), scene: {}'.format(
p['dataset'], p['dataset_split'], p['dataset_split_type'], scene_id))
# Load GT poses.
scene_gt = inout.load_scene_gt(
dp_split['scene_gt_tpath'].format(scene_id=scene_id))
def load(bop_dataset_path: str,
temp_dir: str,
sys_paths: list,
model_type: str = "",
cam_type: str = "",
split: str = "test",
scene_id: int = -1,
obj_ids: list = [],
sample_objects: bool = False,
num_of_objs_to_sample: int = None,
obj_instances_limit: int = -1,
move_origin_to_x_y_plane: bool = False,
source_frame: list = ["X", "-Y", "-Z"],
mm2m: bool = False) -> List[MeshObject]:
""" Loads the 3D models of any BOP dataset and allows replicating BOP scenes
- Interfaces with the bob_toolkit, allows loading of train, val and test splits
- Relative camera poses are loaded/computed with respect to a reference model
- Sets real camera intrinsics
:param bop_dataset_path: Full path to a specific bop dataset e.g. /home/user/bop/tless.
:param temp_dir: A temp directory which is used for writing the temporary .ply file.
:param sys_paths: System paths to append.
:param model_type: Optionally, specify type of BOP model. Available: [reconst, cad or eval].
:param cam_type: Camera type. If not defined, dataset-specific default camera type is used.
:param split: Optionally, test or val split depending on BOP dataset.
:param scene_id: Optionally, specify BOP dataset scene to synthetically replicate. Default: -1 (no scene is replicated,
only BOP Objects are loaded).
:param obj_ids: List of object ids to load. Default: [] (all objects from the given BOP dataset if scene_id is not
specified).
:param sample_objects: Toggles object sampling from the specified dataset.
:param num_of_objs_to_sample: Amount of objects to sample from the specified dataset. If this amount is bigger than the dataset
actually contains, then all objects will be loaded.
:param obj_instances_limit: Limits the amount of object copies when sampling. Default: -1 (no limit).
:param move_origin_to_x_y_plane: Move center of the object to the lower side of the object, this will not work when used in combination with
pose estimation tasks! This is designed for the use-case where BOP objects are used as filler objects in
the background.
:param source_frame: Can be used if the given positions and rotations are specified in frames different from the blender
frame. Has to be a list of three strings. Example: ['X', '-Z', 'Y']: Point (1,2,3) will be transformed
to (1, -3, 2). Available: ['X', 'Y', 'Z', '-X', '-Y', '-Z'].
:param mm2m: Specify whether to convert poses and models to meters.
:return: The list of loaded mesh objects.
"""
for sys_path in sys_paths:
if 'bop_toolkit' in sys_path:
sys.path.append(sys_path)
scale = 0.001 if mm2m else 1
bop_dataset_name = os.path.basename(bop_dataset_path)
has_external_texture = bop_dataset_name in ["ycbv", "ruapc"]
if obj_ids or sample_objects:
allow_duplication = True
else:
allow_duplication = False
datasets_path = os.path.dirname(bop_dataset_path)
dataset = os.path.basename(bop_dataset_path)
print("bob: {}, dataset_path: {}".format(bop_dataset_path,
datasets_path))
print("dataset: {}".format(dataset))
try:
from bop_toolkit_lib import dataset_params, inout
except ImportError as error:
print(
'ERROR: Please download the bop_toolkit package and add it to sys_paths in config!'
)
print('https://github.com/thodan/bop_toolkit')
raise error
model_p = dataset_params.get_model_params(
datasets_path,
dataset,
model_type=model_type if model_type else None)
cam_p = dataset_params.get_camera_params(
datasets_path, dataset, cam_type=cam_type if cam_type else None)
try:
split_p = dataset_params.get_split_params(datasets_path,
dataset,
split=split)
except ValueError:
raise Exception(
"Wrong path or {} split does not exist in {}.".format(
split, dataset))
bpy.context.scene.world["category_id"] = 0
bpy.context.scene.render.resolution_x = cam_p['im_size'][0]
bpy.context.scene.render.resolution_y = cam_p['im_size'][1]
loaded_objects = []
# only load all/selected objects here, use other modules for setting poses
# e.g. camera.CameraSampler / object.ObjectPoseSampler
if scene_id == -1:
# TLESS exception because images are cropped
if bop_dataset_name in ['tless']:
cam_p['K'][0, 2] = split_p['im_size'][0] / 2
cam_p['K'][1, 2] = split_p['im_size'][1] / 2
# set camera intrinsics
CameraUtility.set_intrinsics_from_K_matrix(cam_p['K'],
split_p['im_size'][0],
split_p['im_size'][1])
obj_ids = obj_ids if obj_ids else model_p['obj_ids']
# if sampling is enabled
if sample_objects:
loaded_ids = {}
loaded_amount = 0
if obj_instances_limit != -1 and len(
obj_ids) * obj_instances_limit < num_of_objs_to_sample:
raise RuntimeError(
"{}'s {} split contains {} objects, {} object where requested to sample with "
"an instances limit of {}. Raise the limit amount or decrease the requested "
"amount of objects.".format(bop_dataset_path, split,
len(obj_ids),
num_of_objs_to_sample,
obj_instances_limit))
while loaded_amount != num_of_objs_to_sample:
random_id = choice(obj_ids)
if random_id not in loaded_ids.keys():
loaded_ids.update({random_id: 0})
# if there is no limit or if there is one, but it is not reached for this particular object
if obj_instances_limit == -1 or loaded_ids[
random_id] < obj_instances_limit:
cur_obj = BopLoader._load_mesh(random_id, model_p,
bop_dataset_name,
has_external_texture,
temp_dir,
allow_duplication,
scale)
loaded_ids[random_id] += 1
loaded_amount += 1
loaded_objects.append(cur_obj)
else:
print(
"ID {} was loaded {} times with limit of {}. Total loaded amount {} while {} are "
"being requested".format(random_id,
loaded_ids[random_id],
obj_instances_limit,
loaded_amount,
num_of_objs_to_sample))
else:
for obj_id in obj_ids:
cur_obj = BopLoader._load_mesh(obj_id, model_p,
bop_dataset_name,
has_external_texture,
temp_dir, allow_duplication,
scale)
loaded_objects.append(cur_obj)
# replicate scene: load scene objects, object poses, camera intrinsics and camera poses
else:
sc_gt = inout.load_scene_gt(
split_p['scene_gt_tpath'].format(**{'scene_id': scene_id}))
sc_camera = inout.load_json(
split_p['scene_camera_tpath'].format(**{'scene_id': scene_id}))
for i, (cam_id, insts) in enumerate(sc_gt.items()):
cam_K, cam_H_m2c_ref = BopLoader._get_ref_cam_extrinsics_intrinsics(
sc_camera, cam_id, insts, scale)
if i == 0:
# define world = first camera
cam_H_m2w_ref = cam_H_m2c_ref.copy()
cur_objs = []
# load scene objects and set their poses
for inst in insts:
cur_objs.append(
BopLoader._load_mesh(inst['obj_id'], model_p,
bop_dataset_name,
has_external_texture,
temp_dir, allow_duplication,
scale))
BopLoader.set_object_pose(cur_objs[-1], inst, scale)
cam_H_c2w = BopLoader._compute_camera_to_world_trafo(
cam_H_m2w_ref, cam_H_m2c_ref, source_frame)
# set camera intrinsics
CameraUtility.set_intrinsics_from_K_matrix(
cam_K, split_p['im_size'][0], split_p['im_size'][1])
# set camera extrinsics as next frame
frame_id = CameraUtility.add_camera_pose(cam_H_c2w)
# Add key frame for camera shift, as it changes from frame to frame in the tless replication
cam = bpy.context.scene.camera.data
cam.keyframe_insert(data_path='shift_x', frame=frame_id)
cam.keyframe_insert(data_path='shift_y', frame=frame_id)
# Copy object poses to key frame (to be sure)
for cur_obj in cur_objs:
BopLoader._insert_key_frames(cur_obj, frame_id)
# move the origin of the object to the world origin and on top of the X-Y plane
# makes it easier to place them later on, this does not change the `.location`
# This is only useful if the BOP objects are not used in a pose estimation scenario.
if move_origin_to_x_y_plane:
for obj in loaded_objects:
obj.move_origin_to_bottom_mean_point()
return loaded_objects
# Dataset split type. Options: 'synt', 'real', None = default. See dataset_params.py for options.
'dataset_split_type': None,
# Folder containing the BOP datasets.
'datasets_path': config.datasets_path,
}
################################################################################
datasets_path = p['datasets_path']
dataset_name = p['dataset']
split = p['dataset_split']
split_type = p['dataset_split_type']
dp_split = dataset_params.get_split_params(datasets_path,
dataset_name,
split,
split_type=split_type)
dp_model = dataset_params.get_model_params(datasets_path, dataset_name)
complete_split = split
if dp_split['split_type'] is not None:
complete_split += '_' + dp_split['split_type']
CATEGORIES = [{
'id': obj_id,
'name': str(obj_id),
'supercategory': dataset_name
} for obj_id in dp_model['obj_ids']]
INFO = {
"description": dataset_name + '_' + split,
"url": "https://github.com/thodan/bop_toolkit",
def run(self):
""" Load BOP data """
bop_dataset_path = self.config.get_string("bop_dataset_path")
scene_id = self.config.get_int("scene_id", -1)
obj_ids = self.config.get_list("obj_ids", [])
split = self.config.get_string("split", "test")
model_type = self.config.get_string("model_type", "")
cam_type = self.config.get_string("cam_type", "")
scale = 0.001 if self.config.get_bool("mm2m", False) else 1
datasets_path = os.path.dirname(bop_dataset_path)
dataset = os.path.basename(bop_dataset_path)
print("bob: {}, dataset_path: {}".format(bop_dataset_path,
datasets_path))
print("dataset: {}".format(dataset))
try:
from bop_toolkit_lib import dataset_params, inout
except ImportError as error:
print(
'ERROR: Please download the bop_toolkit package and add it to sys_paths in config!'
)
print('https://github.com/thodan/bop_toolkit')
raise error
model_p = dataset_params.get_model_params(
datasets_path,
dataset,
model_type=model_type if model_type else None)
cam_p = dataset_params.get_camera_params(
datasets_path, dataset, cam_type=cam_type if cam_type else None)
bpy.data.scenes["Scene"]["num_labels"] = len(model_p['obj_ids'])
try:
split_p = dataset_params.get_split_params(datasets_path,
dataset,
split=split)
except ValueError:
raise Exception(
"Wrong path or {} split does not exist in {}.".format(
split, dataset))
bpy.context.scene.world["category_id"] = 0
bpy.context.scene.render.resolution_x = self.config.get_int(
"resolution_x", split_p['im_size'][0])
bpy.context.scene.render.resolution_y = self.config.get_int(
"resolution_y", split_p['im_size'][1])
# Collect camera and camera object
cam_ob = bpy.context.scene.camera
cam = cam_ob.data
cam['loaded_resolution'] = bpy.context.scene.render.resolution_x, bpy.context.scene.render.resolution_y
cam['loaded_intrinsics'] = cam_p[
'K'] # load default intrinsics from camera.json
config = Config({})
camera_module = CameraModule(config)
camera_module._set_cam_intrinsics(cam, config)
#only load all/selected objects here, use other modules for setting poses, e.g. camera.CameraSampler / object.ObjectPoseSampler
if scene_id == -1:
obj_ids = obj_ids if obj_ids else model_p['obj_ids']
for obj_id in obj_ids:
self._load_mesh(obj_id, model_p, scale=scale)
# replicate scene: load scene objects, object poses, camera intrinsics and camera poses
else:
sc_gt = inout.load_scene_gt(
split_p['scene_gt_tpath'].format(**{'scene_id': scene_id}))
sc_camera = inout.load_json(
split_p['scene_camera_tpath'].format(**{'scene_id': scene_id}))
for i, (cam_id, insts) in enumerate(sc_gt.items()):
cam_K, cam_H_m2c_ref = self._get_ref_cam_extrinsics_intrinsics(
sc_camera, cam_id, insts, scale)
if i == 0:
# define world = first camera
cam_H_m2w_ref = cam_H_m2c_ref.copy()
# load scene objects
for inst in insts:
cur_obj = self._load_mesh(inst['obj_id'], model_p)
self.set_object_pose(cur_obj, inst, scale)
cam_H_c2w = self._compute_camera_to_world_trafo(
cam_H_m2w_ref, cam_H_m2c_ref)
#set camera intrinsics and extrinsics
config = Config({
"cam2world_matrix": list(cam_H_c2w.flatten()),
"camK": list(cam_K.flatten())
})
camera_module._set_cam_intrinsics(cam, config)
camera_module._set_cam_extrinsics(cam_ob, config)
# Store new cam pose as next frame
frame_id = bpy.context.scene.frame_end
camera_module._insert_key_frames(cam, cam_ob, frame_id)
bpy.context.scene.frame_end = frame_id + 1
os.path.join('{out_path}', 'scene_gt', '{obj_id:06d}_scene_gt.json')
out_views_vis_tpath =\
os.path.join('{out_path}', 'views_radius', '{obj_id:06d}_views_radius={radius}.ply')
# Load colors.
colors_path = os.path.join(
os.path.dirname(inout.__file__), 'colors.json')
colors = inout.load_json(colors_path)
################################################################################
out_path = out_tpath.format(dataset=dataset)
misc.ensure_dir(out_path)
# Load dataset parameters.
dp_split_test = dataset_params.get_split_params(datasets_path, dataset, 'test')
dp_model = dataset_params.get_model_params(datasets_path, dataset, model_type)
dp_camera = dataset_params.get_camera_params(datasets_path, dataset, cam_type)
if not obj_ids:
obj_ids = dp_model['obj_ids']
# Image size and K for the RGB image (potentially with SSAA).
im_size_rgb = [int(round(x * float(ssaa_fact))) for x in dp_camera['im_size']]
K_rgb = dp_camera['K'] * ssaa_fact
# Intrinsic parameters for RGB rendering.
fx_rgb, fy_rgb, cx_rgb, cy_rgb =\
K_rgb[0, 0], K_rgb[1, 1], K_rgb[0, 2], K_rgb[1, 2]
# Intrinsic parameters for depth rendering.
def run(self):
""" Load BOP data """
datasets_path = os.path.dirname(self.bop_dataset_path)
dataset = os.path.basename(self.bop_dataset_path)
print("bob: {}, dataset_path: {}".format(self.bop_dataset_path,
datasets_path))
print("dataset: {}".format(dataset))
try:
from bop_toolkit_lib import dataset_params, inout
except ImportError as error:
print(
'ERROR: Please download the bop_toolkit package and add it to sys_paths in config!'
)
print('https://github.com/thodan/bop_toolkit')
raise error
model_p = dataset_params.get_model_params(
datasets_path,
dataset,
model_type=self.model_type if self.model_type else None)
cam_p = dataset_params.get_camera_params(
datasets_path,
dataset,
cam_type=self.cam_type if self.cam_type else None)
bpy.data.scenes["Scene"]["num_labels"] = len(model_p['obj_ids'])
try:
split_p = dataset_params.get_split_params(datasets_path,
dataset,
split=self.split)
except ValueError:
raise Exception(
"Wrong path or {} split does not exist in {}.".format(
self.split, dataset))
bpy.context.scene.world["category_id"] = 0
bpy.context.scene.render.resolution_x = split_p['im_size'][0]
bpy.context.scene.render.resolution_y = split_p['im_size'][1]
# Collect camera and camera object
cam_ob = bpy.context.scene.camera
cam = cam_ob.data
cam['loaded_resolution'] = split_p['im_size'][0], split_p['im_size'][1]
# load default intrinsics from camera.json
cam['loaded_intrinsics'] = cam_p['K']
config = Config({})
camera_module = CameraModule(config)
camera_module._set_cam_intrinsics(cam, config)
loaded_objects = []
# only load all/selected objects here, use other modules for setting poses
# e.g. camera.CameraSampler / object.ObjectPoseSampler
if self.scene_id == -1:
obj_ids = self.obj_ids if self.obj_ids else model_p['obj_ids']
# if sampling is enabled
if self.sample_objects:
loaded_ids = {}
loaded_amount = 0
if self.obj_instances_limit != -1 and len(
obj_ids
) * self.obj_instances_limit < self.num_of_objs_to_sample:
raise RuntimeError(
"{}'s {} split contains {} objects, {} object where requested to sample with "
"an instances limit of {}. Raise the limit amount or decrease the requested "
"amount of objects.".format(self.bop_dataset_path,
self.split, len(obj_ids),
self.num_of_objs_to_sample,
self.obj_instances_limit))
while loaded_amount != self.num_of_objs_to_sample:
random_id = choice(obj_ids)
if random_id not in loaded_ids.keys():
loaded_ids.update({random_id: 0})
# if there is no limit or if there is one, but it is not reached for this particular object
if self.obj_instances_limit == -1 or loaded_ids[
random_id] < self.obj_instances_limit:
cur_obj = self._load_mesh(random_id,
model_p,
scale=self.scale)
loaded_ids[random_id] += 1
loaded_amount += 1
loaded_objects.append(cur_obj)
else:
print(
"ID {} was loaded {} times with limit of {}. Total loaded amount {} while {} are "
"being requested".format(
random_id, loaded_ids[random_id],
self.obj_instances_limit, loaded_amount,
self.num_of_objs_to_sample))
else:
for obj_id in obj_ids:
cur_obj = self._load_mesh(obj_id,
model_p,
scale=self.scale)
loaded_objects.append(cur_obj)
self._set_properties(loaded_objects)
# replicate scene: load scene objects, object poses, camera intrinsics and camera poses
else:
sc_gt = inout.load_scene_gt(split_p['scene_gt_tpath'].format(
**{'scene_id': self.scene_id}))
sc_camera = inout.load_json(split_p['scene_camera_tpath'].format(
**{'scene_id': self.scene_id}))
for i, (cam_id, insts) in enumerate(sc_gt.items()):
cam_K, cam_H_m2c_ref = self._get_ref_cam_extrinsics_intrinsics(
sc_camera, cam_id, insts, self.scale)
if i == 0:
# define world = first camera
cam_H_m2w_ref = cam_H_m2c_ref.copy()
cur_objs = []
# load scene objects and set their poses
for inst in insts:
cur_objs.append(
self._load_mesh(inst['obj_id'],
model_p,
scale=self.scale))
self.set_object_pose(cur_objs[-1], inst, self.scale)
cam_H_c2w = self._compute_camera_to_world_trafo(
cam_H_m2w_ref, cam_H_m2c_ref)
#set camera intrinsics and extrinsics
config = Config({
"cam2world_matrix": list(cam_H_c2w.flatten()),
"cam_K": list(cam_K.flatten())
})
camera_module._set_cam_intrinsics(cam, config)
camera_module._set_cam_extrinsics(cam_ob, config)
# Store new cam pose as next frame
frame_id = bpy.context.scene.frame_end
# Copy object poses to next key frame (to be sure)
for cur_obj in cur_objs:
self._insert_key_frames(cur_obj, frame_id)
camera_module._insert_key_frames(cam, cam_ob, frame_id)
bpy.context.scene.frame_end = frame_id + 1
def run(self):
bop_dataset_path = self.config.get_string("bop_dataset_path")
scene_id = self.config.get_int("scene_id")
split = self.config.get_string("split", "test")
model_type = self.config.get_string("model_type", "")
mm2m = 0.001 if self.config.get_bool("mm2m") else 1
datasets_path = os.path.dirname(bop_dataset_path)
dataset = os.path.basename(bop_dataset_path)
print("bob: {}, dataset_path: {}".format(bop_dataset_path,
datasets_path))
print("dataset: {}".format(dataset))
model_p = dataset_params.get_model_params(
datasets_path,
dataset,
model_type=model_type if model_type else None)
camera_p = dataset_params.get_camera_params(datasets_path, dataset)
try:
split_p = dataset_params.get_split_params(datasets_path,
dataset,
split=split)
except ValueError:
raise Exception(
"Wrong path or {} split does not exist in {}.".format(
split, dataset))
sc_gt = inout.load_scene_gt(
split_p['scene_gt_tpath'].format(**{'scene_id': scene_id}))
sc_camera = inout.load_json(
split_p['scene_camera_tpath'].format(**{'scene_id': scene_id}))
bpy.context.scene.render.resolution_x = self.config.get_int(
"resolution_x", split_p['im_size'][0])
bpy.context.scene.render.resolution_y = self.config.get_int(
"resolution_y", split_p['im_size'][1])
#bpy.context.scene.render.pixel_aspect_x = self.config.get_float("pixel_aspect_x", 1) #split_p['im_size'][0] / split_p['im_size'][1])
cm = CameraModule(self.config)
for i, (cam_id, insts) in enumerate(sc_gt.items()):
cam_K = np.array(sc_camera[str(cam_id)]['cam_K']).reshape(3, 3)
cam_H_m2c_ref = np.eye(4)
cam_H_m2c_ref[:3, :3] = np.array(insts[0]['cam_R_m2c']).reshape(
3, 3)
cam_H_m2c_ref[:3, 3] = np.array(
insts[0]['cam_t_m2c']).reshape(3) * mm2m
if i == 0:
# define world = first camera
cam_H_m2w_ref = cam_H_m2c_ref.copy()
for inst in insts:
bpy.ops.import_mesh.ply(
filepath=model_p['model_tpath'].format(
**{'obj_id': inst['obj_id']}))
cam_H_m2c = np.eye(4)
cam_H_m2c[:3, :3] = np.array(inst['cam_R_m2c']).reshape(
3, 3)
cam_H_m2c[:3, 3] = np.array(
inst['cam_t_m2c']).reshape(3) * mm2m
# world = camera @ i=0
cam_H_m2w = cam_H_m2c
print('-----------------------------')
print("Model: {}".format(cam_H_m2w))
print('-----------------------------')
cur_obj = bpy.context.selected_objects[-1]
cur_obj.matrix_world = Matrix(cam_H_m2w)
cur_obj.scale = Vector((mm2m, mm2m, mm2m))
mat = self._load_materials(cur_obj)
self._link_col_node(mat)
cam_H_c2w = np.dot(cam_H_m2w_ref, np.linalg.inv(cam_H_m2c_ref))
print('-----------------------------')
print("Cam: {}".format(cam_H_c2w))
print('-----------------------------')
config = {"location": [0, 0, 0], "rotation": list([0, 0, 0])}
cm._add_cam_pose(Config(config), Matrix(cam_H_c2w), cam_K)
def run(self):
""" Load BOP data """
datasets_path = os.path.dirname(self.bop_dataset_path)
dataset = os.path.basename(self.bop_dataset_path)
print("bob: {}, dataset_path: {}".format(self.bop_dataset_path,
datasets_path))
print("dataset: {}".format(dataset))
try:
from bop_toolkit_lib import dataset_params, inout
except ImportError as error:
print(
'ERROR: Please download the bop_toolkit package and add it to sys_paths in config!'
)
print('https://github.com/thodan/bop_toolkit')
raise error
model_p = dataset_params.get_model_params(
datasets_path,
dataset,
model_type=self.model_type if self.model_type else None)
cam_p = dataset_params.get_camera_params(
datasets_path,
dataset,
cam_type=self.cam_type if self.cam_type else None)
try:
split_p = dataset_params.get_split_params(datasets_path,
dataset,
split=self.split)
except ValueError:
raise Exception(
"Wrong path or {} split does not exist in {}.".format(
self.split, dataset))
bpy.context.scene.world["category_id"] = 0
bpy.context.scene.render.resolution_x = cam_p['im_size'][0]
bpy.context.scene.render.resolution_y = cam_p['im_size'][1]
loaded_objects = []
# only load all/selected objects here, use other modules for setting poses
# e.g. camera.CameraSampler / object.ObjectPoseSampler
if self.scene_id == -1:
# TLESS exception because images are cropped
if self.bop_dataset_name in ['tless']:
cam_p['K'][0, 2] = split_p['im_size'][0] / 2
cam_p['K'][1, 2] = split_p['im_size'][1] / 2
# set camera intrinsics
CameraUtility.set_intrinsics_from_K_matrix(cam_p['K'],
split_p['im_size'][0],
split_p['im_size'][1])
obj_ids = self.obj_ids if self.obj_ids else model_p['obj_ids']
# if sampling is enabled
if self.sample_objects:
loaded_ids = {}
loaded_amount = 0
if self.obj_instances_limit != -1 and len(
obj_ids
) * self.obj_instances_limit < self.num_of_objs_to_sample:
raise RuntimeError(
"{}'s {} split contains {} objects, {} object where requested to sample with "
"an instances limit of {}. Raise the limit amount or decrease the requested "
"amount of objects.".format(self.bop_dataset_path,
self.split, len(obj_ids),
self.num_of_objs_to_sample,
self.obj_instances_limit))
while loaded_amount != self.num_of_objs_to_sample:
random_id = choice(obj_ids)
if random_id not in loaded_ids.keys():
loaded_ids.update({random_id: 0})
# if there is no limit or if there is one, but it is not reached for this particular object
if self.obj_instances_limit == -1 or loaded_ids[
random_id] < self.obj_instances_limit:
cur_obj = self._load_mesh(random_id,
model_p,
scale=self.scale)
loaded_ids[random_id] += 1
loaded_amount += 1
loaded_objects.append(cur_obj)
else:
print(
"ID {} was loaded {} times with limit of {}. Total loaded amount {} while {} are "
"being requested".format(
random_id, loaded_ids[random_id],
self.obj_instances_limit, loaded_amount,
self.num_of_objs_to_sample))
else:
for obj_id in obj_ids:
cur_obj = self._load_mesh(obj_id,
model_p,
scale=self.scale)
loaded_objects.append(cur_obj)
self._set_properties(loaded_objects)
# replicate scene: load scene objects, object poses, camera intrinsics and camera poses
else:
sc_gt = inout.load_scene_gt(split_p['scene_gt_tpath'].format(
**{'scene_id': self.scene_id}))
sc_camera = inout.load_json(split_p['scene_camera_tpath'].format(
**{'scene_id': self.scene_id}))
for i, (cam_id, insts) in enumerate(sc_gt.items()):
cam_K, cam_H_m2c_ref = self._get_ref_cam_extrinsics_intrinsics(
sc_camera, cam_id, insts, self.scale)
if i == 0:
# define world = first camera
cam_H_m2w_ref = cam_H_m2c_ref.copy()
cur_objs = []
# load scene objects and set their poses
for inst in insts:
cur_objs.append(
self._load_mesh(inst['obj_id'],
model_p,
scale=self.scale))
self.set_object_pose(cur_objs[-1], inst, self.scale)
cam_H_c2w = self._compute_camera_to_world_trafo(
cam_H_m2w_ref, cam_H_m2c_ref)
# set camera intrinsics
CameraUtility.set_intrinsics_from_K_matrix(
cam_K, split_p['im_size'][0], split_p['im_size'][1])
# set camera extrinsics as next frame
frame_id = CameraUtility.add_camera_pose(cam_H_c2w)
# Add key frame for camera shift, as it changes from frame to frame in the tless replication
cam = bpy.context.scene.camera.data
cam.keyframe_insert(data_path='shift_x', frame=frame_id)
cam.keyframe_insert(data_path='shift_y', frame=frame_id)
# Copy object poses to key frame (to be sure)
for cur_obj in cur_objs:
self._insert_key_frames(cur_obj, frame_id)
# move the origin of the object to the world origin and on top of the X-Y plane
# makes it easier to place them later on, this does not change the `.location`
# This is only useful if the BOP objects are not used in a pose estimation scenario.
move_to_origin = self.config.get_bool("move_origin_to_x_y_plane",
False)
if move_to_origin:
LoaderInterface.move_obj_origin_to_bottom_mean_point(
loaded_objects)
'w') #gt file for training keras-retinanet
l_writer = csv.writer(label_file, delimiter=",")
csvfile = open(os.path.join(bop_dir, "retinanet_gt.csv"),
'w') #gt file for training keras-retinanet
writer = csv.writer(csvfile, delimiter=",")
for m_idx, m_id in enumerate(model_ids):
l_writer.writerow([m_id, m_idx])
label_file.close()
if (dataset != 'ycbv'):
print("Loading...", dataset)
im_width, im_height = cam_param_global['im_size']
test_params = dataset_params.get_split_params(bop_dir, dataset, "test")
if (test_params['depth_range'] is not None):
mean_depth = np.mean(test_params['depth_range']) / 1000
max_depth = test_params['depth_range'][1] / 1000
elif (dataset == "itodd"):
mean_depth = (0.601 + 1.102) / 2
max_depth = 1.102
else:
mean_depth = 1 #1default depth= 1
max_depth = 3
#build map from class_id(used in Detection pipeline) to actual obj_id (in GT)
#detection of i+1 label means detetction of model number: model_ids[i]
crop_dir = bop_dir + "/train_crop"
cropmask_dir = bop_dir + "/train_cropmask"
def run(self):
bop_dataset_path = self.config.get_string("bop_dataset_path")
scene_id = self.config.get_int("scene_id", -1)
obj_ids = self.config.get_list("obj_ids", [])
split = self.config.get_string("split", "test")
model_type = self.config.get_string("model_type", "")
cam_type = self.config.get_string("cam_type", "")
mm2m = 0.001 if self.config.get_bool("mm2m", False) else 1
datasets_path = os.path.dirname(bop_dataset_path)
dataset = os.path.basename(bop_dataset_path)
print("bob: {}, dataset_path: {}".format(bop_dataset_path,
datasets_path))
print("dataset: {}".format(dataset))
try:
from bop_toolkit_lib import dataset_params, inout
except ImportError as error:
print(
'ERROR: Please download the bop_toolkit package and add it to sys_paths in config!'
)
print('https://github.com/thodan/bop_toolkit')
raise error
model_p = dataset_params.get_model_params(
datasets_path,
dataset,
model_type=model_type if model_type else None)
cam_p = dataset_params.get_camera_params(
datasets_path, dataset, cam_type=cam_type if cam_type else None)
bpy.data.scenes["Scene"]["num_labels"] = len(model_p['obj_ids'])
try:
split_p = dataset_params.get_split_params(datasets_path,
dataset,
split=split)
except ValueError:
raise Exception(
"Wrong path or {} split does not exist in {}.".format(
split, dataset))
bpy.context.scene.world["category_id"] = 0
bpy.context.scene.render.resolution_x = self.config.get_int(
"resolution_x", split_p['im_size'][0])
bpy.context.scene.render.resolution_y = self.config.get_int(
"resolution_y", split_p['im_size'][1])
# Collect camera and camera object
cam_ob = bpy.context.scene.camera
cam = cam_ob.data
cam['loaded_resolution'] = bpy.context.scene.render.resolution_x, bpy.context.scene.render.resolution_y
cam['loaded_intrinsics'] = cam_p[
'K'] # load default intrinsics from camera.json
#only load all/selected objects here, use other modules for setting poses, e.g. camera.CameraSampler / object.ObjectPoseSampler
if scene_id == -1:
obj_ids = obj_ids if obj_ids else model_p['obj_ids']
for obj_id in obj_ids:
self._load_mesh(obj_id, model_p, mm2m=mm2m)
# replicate scene: load scene objects, object poses, camera intrinsics and camera poses
else:
sc_gt = inout.load_scene_gt(
split_p['scene_gt_tpath'].format(**{'scene_id': scene_id}))
sc_camera = inout.load_json(
split_p['scene_camera_tpath'].format(**{'scene_id': scene_id}))
cm = CameraModule(self.config)
for i, (cam_id, insts) in enumerate(sc_gt.items()):
cam_K = np.array(sc_camera[str(cam_id)]['cam_K']).reshape(3, 3)
cam_H_m2c_ref = np.eye(4)
cam_H_m2c_ref[:3, :3] = np.array(
insts[0]['cam_R_m2c']).reshape(3, 3)
cam_H_m2c_ref[:3, 3] = np.array(
insts[0]['cam_t_m2c']).reshape(3) * mm2m
if i == 0:
# define world = first camera
cam_H_m2w_ref = cam_H_m2c_ref.copy()
for inst in insts:
cur_obj = self._load_mesh(inst['obj_id'],
model_p,
mm2m=mm2m)
cam_H_m2c = np.eye(4)
cam_H_m2c[:3, :3] = np.array(
inst['cam_R_m2c']).reshape(3, 3)
cam_H_m2c[:3, 3] = np.array(
inst['cam_t_m2c']).reshape(3) * mm2m
# world = camera @ i=0
cam_H_m2w = cam_H_m2c
print('-----------------------------')
print("Model: {}".format(cam_H_m2w))
print('-----------------------------')
cur_obj.matrix_world = Matrix(cam_H_m2w)
cam_H_c2w = np.dot(cam_H_m2w_ref, np.linalg.inv(cam_H_m2c_ref))
print('-----------------------------')
print("Cam: {}".format(cam_H_c2w))
print('-----------------------------')
config = {"location": [0, 0, 0], "rotation": list([0, 0, 0])}
cm._add_cam_pose(Config(config), Matrix(cam_H_c2w), cam_K)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
# Load the list examples.
examples_path = os.path.join(config.TF_DATA_PATH, 'example_lists',
FLAGS.examples_filename)
tf.logging.info(
'Loading a list of examples from: {}'.format(examples_path))
examples_list = tfrecord.load_example_list(examples_path)
# Load dataset parameters.
dp_split = dataset_params.get_split_params(config.BOP_PATH, FLAGS.dataset,
FLAGS.split, FLAGS.split_type)
# Pre-load camera parameters and ground-truth annotations.
scene_gt = {}
scene_gt_info = {}
scene_camera = {}
scene_ids = set([e['scene_id'] for e in examples_list])
for scene_id in scene_ids:
scene_camera[scene_id] = inout.load_scene_camera(
dp_split['scene_camera_tpath'].format(scene_id=scene_id))
if FLAGS.add_gt:
scene_gt[scene_id] = inout.load_scene_gt(
dp_split['scene_gt_tpath'].format(scene_id=scene_id))
scene_gt_info[scene_id] = inout.load_json(
dp_split['scene_gt_info_tpath'].format(scene_id=scene_id),
keys_to_int=True)
# Check the name of the file with examples.
examples_end = '_examples.txt'
if not FLAGS.examples_filename.endswith(examples_end):
raise ValueError(
'Name of the file with examples must end with {}.'.format(
examples_end))
# Prepare writer of the TFRecord file.
output_name = FLAGS.examples_filename.split(examples_end)[0]
output_path = os.path.join(FLAGS.output_dir, output_name + '.tfrecord')
writer = tf.python_io.TFRecordWriter(output_path)
tf.logging.info('File to be created: {}'.format(output_path))
# Optionally shuffle the examples.
if FLAGS.shuffle:
random.shuffle(examples_list)
# Write the examples to the TFRecord file.
w_start_t = time.time()
create_tf_example_partial = partial(create_tf_example,
dp_split=dp_split,
scene_camera=scene_camera,
scene_gt=scene_gt,
scene_gt_info=scene_gt_info)
for example_id, example in enumerate(examples_list):
if example_id % 50 == 0:
tf.logging.info('Processing example {}/{}'.format(
example_id + 1, len(examples_list)))
tf_example, _ = create_tf_example_partial(example)
writer.write(tf_example)
# Close the writer.
writer.close()
w_total_t = time.time() - w_start_t
tf.logging.info('Writing took {} s.'.format(w_total_t))
# PARAMETERS.
################################################################################
p = {
# See dataset_params.py for options.
'dataset': 'lm',
# Type of input object models.
'model_type': 'eval',
# Folder containing the BOP datasets.
'datasets_path': config.datasets_path,
}
################################################################################
# Load dataset parameters.
dp_split = dataset_params.get_split_params(p['datasets_path'], p['dataset'],
'train')
dp_model = dataset_params.get_model_params(p['datasets_path'], p['dataset'],
p['model_type'])
models_info = {}
for obj_id in dp_model['obj_ids']:
misc.log('Processing model of object {}...'.format(obj_id))
model = inout.load_ply(dp_model['model_tpath'].format(obj_id=obj_id))
# Calculate 3D bounding box.
ref_pt = map(float, model['pts'].min(axis=0).flatten())
size = map(float, (model['pts'].max(axis=0) - ref_pt).flatten())
# Calculated diameter.