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))
os.path.join('{out_path}', '{obj_id:06d}', 'rgb', '{im_id:06d}.png')
out_depth_tpath =\
os.path.join('{out_path}', '{obj_id:06d}', 'depth', '{im_id:06d}.png')
out_scene_camera_tpath =\
os.path.join('{out_path}', '{obj_id:06d}', 'scene_camera.json')
out_scene_gt_tpath =\
os.path.join('{out_path}', '{obj_id:06d}', 'scene_gt.json')
out_views_vis_tpath =\
os.path.join('{out_path}', '{obj_id:06d}', 'views_radius={radius}.ply')
################################################################################
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.