def get_camera_params(datasets_path, dataset_name, cam_type=None):
"""Returns camera parameters for the specified dataset.
Note that parameters returned by this functions are meant only for simulation
of the used sensor when rendering training images. To get per-image camera
parameters (which may vary), use path template 'scene_camera_tpath' contained
in the dictionary returned by function get_split_params.
:param datasets_path: Path to a folder with datasets.
:param dataset_name: Name of the dataset for which to return the parameters.
:param cam_type: Type of camera.
:return: Dictionary with camera parameters for the specified dataset.
"""
if dataset_name == 'tless':
# Includes images captured by three sensors. Use Primesense as default.
if cam_type is None:
cam_type = 'primesense'
cam_filename = 'camera_{}.json'.format(cam_type)
elif dataset_name in ['hbs', 'hb']:
# Both versions of the HB dataset share the same directory.
dataset_name = 'hb'
# Includes images captured by two sensors. Use Primesense as default.
if cam_type is None:
cam_type = 'primesense'
cam_filename = 'camera_{}.json'.format(cam_type)
elif dataset_name == 'ycbv':
# Includes images captured by two sensors. Use the "UW" sensor as default.
if cam_type is None:
cam_type = 'uw'
cam_filename = 'camera_{}.json'.format(cam_type)
else:
cam_filename = 'camera.json'
# Path to the camera file.
cam_params_path = join(datasets_path, dataset_name, cam_filename)
p = {
# Path to a file with camera parameters.
'cam_params_path': cam_params_path,
}
# Add a dictionary containing the intrinsic camera matrix ('K'), image size
# ('im_size'), and scale of the depth images ('depth_scale', optional).
p.update(inout.load_cam_params(cam_params_path))
return p
def get_dataset(cfg,dataset,train=True,incl_param=False,eval=False,eval_model=False):
#return serialized datset information
bop_dir = cfg['dataset_dir']
if eval_model:
postfix_model = '_eval'
else:
postfix_model = ''
if(dataset=='lmo'):
bop_dataset_dir = os.path.join(bop_dir,"lmo")
test_dir = bop_dataset_dir+"/test"
train_dir = bop_dataset_dir+"/train"
model_dir = bop_dataset_dir+"/models"+postfix_model
model_scale=0.001
elif(dataset=='ruapc'):
bop_dataset_dir = os.path.join(bop_dir,"ruapc")
test_dir = bop_dataset_dir+"/test"
train_dir = bop_dataset_dir+"/train"
model_dir = bop_dataset_dir+"/models"+postfix_model
model_scale=0.001
elif(dataset=='hb'):
bop_dataset_dir = os.path.join(bop_dir,"hb")
test_dir = bop_dataset_dir+"/test"
train_dir = bop_dataset_dir+"/train"
model_dir = bop_dataset_dir+"/models"+postfix_model
model_scale=0.0001
elif(dataset=='icbin'):
bop_dataset_dir = os.path.join(bop_dir,"icbin")
test_dir = bop_dataset_dir+"/test"
train_dir = bop_dataset_dir+"/train"
model_dir = bop_dataset_dir+"/models"+postfix_model
model_scale=0.001
elif(dataset=='itodd'):
bop_dataset_dir = os.path.join(bop_dir,"itodd")
test_dir = bop_dataset_dir+"/test"
train_dir = bop_dataset_dir+"/train"
model_dir = bop_dataset_dir+"/models"+postfix_model
model_scale=0.001
elif(dataset=='tudl'):
bop_dataset_dir = os.path.join(bop_dir,"tudl")
test_dir = bop_dataset_dir+"/test"
train_dir = bop_dataset_dir+"/train_real"
model_dir = bop_dataset_dir+"/models"+postfix_model
model_scale=0.001
elif(dataset=='tless'):
bop_dataset_dir = os.path.join(bop_dir,"tless")
test_dir = bop_dataset_dir+"/test_primesense"
train_dir = bop_dataset_dir+"/train_primesense"
if not(train) and not(eval_model):
model_dir = bop_dataset_dir+"/models_reconst" #use this only for vis
elif eval_model:
model_dir = bop_dataset_dir+"/models_eval"
else:
model_dir = bop_dataset_dir+"/models_cad"
model_scale=0.001
elif(dataset=='ycbv'):
bop_dataset_dir = os.path.join(bop_dir,"ycbv")
test_dir = bop_dataset_dir+"/test"
train_dir = bop_dataset_dir+"/train"
model_dir = bop_dataset_dir+"/models"+postfix_model
model_scale=0.001
elif(dataset=='lm'):
bop_dataset_dir = os.path.join(bop_dir,"lm")
test_dir = bop_dataset_dir+"/test"
train_dir = bop_dataset_dir+"/train"
model_dir = bop_dataset_dir+"/models"+postfix_model
model_dir = "/home/kiru/media/hdd_linux/PoseDataset/hinterstoisser/model_eval"
model_scale=0.001
model_info = inout.load_json(os.path.join(model_dir,"models_info.json"))
if(dataset=='ycbv'):
cam_param_global = inout.load_cam_params(os.path.join(bop_dataset_dir,"camera_uw.json"))
else:
cam_param_global = inout.load_cam_params(os.path.join(bop_dataset_dir,"camera.json"))
im_size=np.array(cam_param_global['im_size'])[::-1]
model_plys=[]
rgb_files=[]
depth_files=[]
mask_files=[]
gts=[]
params=[]
model_ids = []
for model_id in model_info.keys():
ply_fn = os.path.join(model_dir,"obj_{:06d}.ply".format(int(model_id)))
if(os.path.exists(ply_fn)): model_ids.append(int(model_id)) #add model id only if the model.ply file exists
model_ids = np.sort(np.array(model_ids))
for model_id in model_ids:
ply_fn = os.path.join(model_dir,"obj_{:06d}.ply".format(int(model_id)))
model_plys.append(ply_fn)
print(model_id,ply_fn)
print("if models are not fully listed above, please make sure there are ply files available")
if(train):
target_dir =train_dir
if(os.path.exists(target_dir)):
for dir in os.listdir(target_dir): #loop over a seqeunce
current_dir = target_dir+"/"+dir
if os.path.exists(os.path.join(current_dir,"scene_camera.json")):
scene_params = inout.load_scene_camera(os.path.join(current_dir,"scene_camera.json"))
scene_gt_fn = os.path.join(current_dir,"scene_gt.json")
has_gt=False
if os.path.exists(scene_gt_fn):
scene_gts = inout.load_scene_gt(scene_gt_fn)
has_gt=True
for img_id in sorted(scene_params.keys()):
im_id = int(img_id)
if(dataset=="itodd" and not(train)):
rgb_fn = os.path.join(current_dir+"/gray","{:06d}.tif".format(im_id))
else:
rgb_fn = os.path.join(current_dir+"/rgb","{:06d}.png".format(im_id))
depth_fn = os.path.join(current_dir+"/depth","{:06d}.png".format(im_id))
if(train):
if(dataset=='hb' or dataset=='itodd' or dataset=='ycbv'):
mask_fn = os.path.join(current_dir+"/mask","{:06d}.png".format(im_id))
else:
mask_fn = os.path.join(current_dir+"/mask","{:06d}_000000.png".format(im_id))
mask_files.append(mask_fn)
rgb_files.append(rgb_fn)
depth_files.append(depth_fn)
if(has_gt):gts.append(scene_gts[im_id])
params.append(scene_params[im_id])
else:
target_dir =test_dir
if(incl_param):
return bop_dataset_dir,target_dir,model_plys,model_info,model_ids,rgb_files,depth_files,mask_files,gts,cam_param_global,params
else:
return bop_dataset_dir,target_dir,model_plys,model_info,model_ids,rgb_files,depth_files,mask_files,gts,cam_param_global