def __init__(self, split):
if split == 'test':
args = DatasetCatalog.get(cfg.test.dataset)
else:
args = DatasetCatalog.get(cfg.train.dataset)
self.ann_file = args['ann_file']
self.coco = coco.COCO(self.ann_file)
def _evaluator_factory(cfg):
task = cfg.task
data_source = DatasetCatalog.get(cfg.test.dataset)['id']
module = '.'.join(['lib.evaluators', data_source, task])
path = os.path.join('lib/evaluators', data_source, task+'.py')
evaluator = imp.load_source(module, path).Evaluator(cfg.result_dir)
return evaluator
def __init__(self, result_dir):
self.result_dir = os.path.join(result_dir, cfg.test.dataset)
os.system('mkdir -p {}'.format(self.result_dir))
args = DatasetCatalog.get(cfg.test.dataset)
self.ann_file = args['ann_file']
self.coco = coco.COCO(self.ann_file)
data_root = args['data_root']
cls = cfg.cls_type
model_path = os.path.join('data/linemod', cls, cls + '.ply')
self.model = pvnet_data_utils.get_ply_model(model_path)
self.diameter = linemod_config.diameters[cls] / 100
self.proj2d = []
self.add = []
self.cmd5 = []
self.icp_proj2d = []
self.icp_add = []
self.icp_cmd5 = []
self.mask_ap = []
self.height = 480
self.width = 640
model = inout.load_ply(model_path)
model['pts'] = model['pts'] * 1000
self.icp_refiner = icp_utils.ICPRefiner(
model, (self.width, self.height)) if cfg.test.icp else None
def make_visualizer(cfg):
task = cfg.task
data_source = DatasetCatalog.get(cfg.test.dataset)['id']
module = '.'.join(['lib.visualizers', data_source, task])
path = os.path.join('lib/visualizers', data_source, task+'.py')
visualizer = imp.load_source(module, path).Visualizer()
return visualizer
def __init__(self, result_dir):
self.result_dir = result_dir
args = DatasetCatalog.get(cfg.test.dataset)
self.ann_file = args['ann_file']
self.coco = coco.COCO(self.ann_file)
data_root = args['data_root']
model_path = 'data/custom/model.ply'
self.model = pvnet_data_utils.get_ply_model(model_path)
self.diameter = np.loadtxt('data/custom/diameter.txt').item()
self.proj2d = []
self.add = []
self.icp_add = []
self.cmd5 = []
self.mask_ap = []
self.icp_render = icp_utils.SynRenderer(
cfg.cls_type) if cfg.test.icp else None
def __init__(self, result_dir):
self.results = []
self.img_ids = []
self.aps = []
self.result_dir = result_dir
os.system('mkdir -p {}'.format(self.result_dir))
args = DatasetCatalog.get(cfg.test.dataset)
ann_file = 'data/ctw/test/ctw_val_instance.json'
self.coco = coco.COCO(ann_file)
self.json_category_id_to_contiguous_id = {
v: i for i, v in enumerate(self.coco.getCatIds())
}
self.contiguous_category_id_to_json_id = {
v: k for k, v in self.json_category_id_to_contiguous_id.items()
}
def __init__(self, result_dir):
self.result_dir = result_dir
args = DatasetCatalog.get(cfg.test.dataset)
self.ann_file = args['ann_file']
self.coco = coco.COCO(self.ann_file)
data_root = args['data_root']
cls = cfg.cls_type
model_path = os.path.join('data/linemod', cls, cls + '.ply')
self.model = pvnet_data_utils.get_ply_model(model_path)
self.diameter = linemod_config.diameters[cls] / 100
self.proj2d = []
self.add = []
self.icp_add = []
self.cmd5 = []
self.mask_ap = []
self.icp_render = icp_utils.SynRenderer(
cfg.cls_type) if cfg.test.icp else None
def __init__(self, result_dir):
self.result_dir = result_dir
args = DatasetCatalog.get(cfg.test.dataset)
self.ann_file = args['ann_file']
self.coco = coco.COCO(self.ann_file)
data_root = args['data_root']
model_path = os.path.join(data_root, 'model.ply')
self.model = pvnet_data_utils.get_ply_model(model_path)
self.diameter = np.loadtxt(os.path.join(data_root, 'diameter.txt')).item()
self.proj2d = []
self.add = []
self.icp_add = []
self.cmd5 = []
self.translational_error = [] # in cm
self.rotational_error = [] # in degrees
self.mask_ap = []
self.data = {}
self.icp_render = icp_utils.SynRenderer(cfg.cls_type) if cfg.test.icp else None
def __init__(self, result_dir):
self.result_dir = os.path.join(result_dir, cfg.test.dataset)
os.system('mkdir -p {}'.format(self.result_dir))
args = DatasetCatalog.get(cfg.test.dataset)
self.ann_file = args['ann_file']
self.coco = coco.COCO(self.ann_file)
data_root = args['data_root']
cls = cfg.cls_type
self.cls = cls
model_path = os.path.join('data/linemod', cls, cls + '.ply')
self.model = pvnet_data_utils.get_ply_model(model_path)
# with open('/mbrdi/sqnap1_colomirror/gupansh/ape_model.pkl','wb') as fp:
# pickle.dump(self.model, fp)
# print('saved..')
# input()
self.diameter = linemod_config.diameters[cls] / 100
self.proj2d = []
self.add = []
self.cmd5 = []
self.icp_proj2d = []
self.icp_add = []
self.icp_cmd5 = []
self.mask_ap = []
self.height = 480
self.width = 640
if cfg.test.icp:
self.icp_refiner = ext_.Synthesizer(os.path.realpath(model_path))
self.icp_refiner.setup(self.width, self.height)
with open('data/linemod/' + cls + '/translation_min.pkl', 'rb') as fp:
self.translation_min = pickle.load(fp)
with open('data/linemod/' + cls + '/translation_max.pkl', 'rb') as fp:
self.translation_max = pickle.load(fp)
def __init__(self, result_dir):
self.result_dir = result_dir
args = DatasetCatalog.get(cfg.test.dataset)
self.ann_file = args['ann_file']
self.obj_id = int(args['obj_id'])
self.coco = coco.COCO(self.ann_file)
self.gt_img_ids = self.coco.getImgIds(catIds=[self.obj_id])
model_dir = 'data/tless/models_cad'
obj_path = os.path.join(model_dir,
'obj_{:02d}.ply'.format(self.obj_id))
self.model = inout.load_ply(obj_path)
self.model_pts = self.model['pts'] / 1000.
model_info = yaml.load(open(os.path.join(model_dir,
'models_info.yml')))
self.diameter = model_info[self.obj_id]['diameter'] / 1000.
self.vsd = []
self.adi = []
self.cmd5 = []
self.icp_vsd = []
self.icp_adi = []
self.icp_cmd5 = []
self.pose_per_id = []
self.pose_icp_per_id = []
self.img_ids = []
self.height = 540
self.width = 720
if cfg.test.icp or cfg.test.vsd:
self.icp_refiner = icp_utils.ICPRefiner(self.model,
(self.width, self.height))
def __init__(self):
args = DatasetCatalog.get(cfg.test.dataset)
self.ann_file = args['ann_file']
self.coco = coco.COCO(self.ann_file)
def __init__(self):
args = DatasetCatalog.get(cfg.test.dataset)
self.ann_file = args['ann_file']
self.coco = coco.COCO(self.ann_file)
self.mesh = trimesh.load(os.path.join(args['data_root'], "model.ply"))
