def get_cfg(nopause=False):
cfg = get_inp_cfg()
# outputs
cfg['target_num_channels'] = 1
cfg['target_dim'] = (256, 256) # (1024, 1024)
cfg['target_domain_name'] = 'depth_zbuffer'
cfg['target_preprocessing_fn'] = load_ops.resize_and_rescale_image_log
cfg['target_preprocessing_fn_kwargs'] = {
'new_dims': cfg['target_dim'],
'offset': 1.,
'normalizer': np.log(2.**16.0)
}
# masks
cfg['mask_fn'] = mask_if_channel_ge # given target image as input
cfg['mask_fn_kwargs'] = {
'img': '<TARGET_IMG>',
'channel_idx': 0,
'threshhold':
64500, # roughly max value - 1000. This margin is for interpolation errors
'broadcast_to_dim': cfg['target_num_channels']
}
#cfg['depth_mask'] = True
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
def get_cfg(nopause=False):
cfg = get_inp_cfg()
cfg['is_discriminative'] = True
cfg['single_filename_to_multiple'] = True
# outputs
cfg['target_dim'] = 9 # (1024, 1024)
cfg['target_from_filenames'] = load_ops.vanishing_point_well_defined
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
def get_cfg( nopause=False ):
cfg = get_inp_cfg()
cfg['is_discriminative'] = True
cfg['single_filename_to_multiple']=True
# outputs
cfg['target_dim'] = 63 # (1024, 1024)
cfg['target_from_filenames'] = load_ops.class_places_workspace_and_home
cfg['mask_by_target_func'] = True
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
def get_cfg(nopause=False):
cfg = get_inp_cfg()
# outputs
cfg['target_num_channels'] = 2
cfg['target_dim'] = (256, 256) # (1024, 1024)
cfg['target_domain_name'] = 'principal_curvature'
cfg['target_preprocessing_fn'] = load_ops.curvature_preprocess
cfg['target_preprocessing_fn_kwargs'] = {'new_dims': cfg['target_dim']}
# masks
cfg['depth_mask'] = True
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
def get_cfg( nopause=False ):
cfg = get_inp_cfg()
# outputs
cfg['target_num_channels'] = 3
cfg['target_dim'] = (256, 256) # (1024, 1024)
cfg['target_domain_name'] = 'rgb'
cfg['target_preprocessing_fn'] = load_ops.resize_rescale_image
cfg['target_preprocessing_fn_kwargs'] = {
'new_dims': cfg['target_dim'],
'new_scale': [-1, 1]
}
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
def get_cfg(nopause=False):
cfg = get_inp_cfg()
# outputs
cfg['target_num_channels'] = 1
cfg['target_dim'] = (256, 256) # (1024, 1024)
cfg['target_domain_name'] = 'depth_euclidean'
cfg['target_preprocessing_fn'] = load_ops.resize_and_rescale_image_log
cfg['target_preprocessing_fn_kwargs'] = {
'new_dims': cfg['target_dim'],
'offset': 1.,
'normalizer': np.log(2.**16.0)
}
# masks
cfg['depth_mask'] = True
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
def get_cfg(nopause=False):
cfg = get_inp_cfg()
# outputs
cfg['target_num_channels'] = 1
cfg['target_dim'] = (256, 256) # (1024, 1024)
cfg['target_domain_name'] = 'edge_occlusion'
cfg['target_preprocessing_fn'] = load_ops.resize_rescale_image_gaussian_blur
cfg['target_preprocessing_fn_kwargs'] = {
'new_dims': cfg['target_dim'],
'new_scale': [-1, 1],
'current_scale': [0.0, 0.00625],
'no_clip': True
}
cfg['depth_mask'] = True
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
def get_cfg(nopause=False):
cfg = get_inp_cfg()
# outputs
cfg['target_num_channels'] = 1
cfg['target_dim'] = (256, 256) # (1024, 1024)
cfg['target_domain_name'] = 'keypoints2d'
cfg['target_preprocessing_fn'] = load_ops.resize_rescale_image
cfg['target_preprocessing_fn_kwargs'] = {
'new_dims': cfg['target_dim'],
'new_scale': [-1, 1],
'current_scale': [0.0, 0.005]
}
# masks
#cfg['depth_mask'] = True
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
def get_cfg(nopause=False):
cfg = get_inp_cfg()
# outputs
cfg['num_pixels'] = 300
cfg['only_target_discriminative'] = True
cfg['target_num_channels'] = 64
cfg['target_dim'] = (cfg['num_pixels'], 3) # (1024, 1024)
cfg['target_domain_name'] = 'segment_unsup2d'
cfg['target_from_filenames'] = load_ops.segment_pixel_sample
cfg['target_from_filenames_kwargs'] = {
'new_dims': (256, 256),
'num_pixels': cfg['num_pixels'],
'domain': cfg['target_domain_name']
}
cfg['return_accuracy'] = False
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
def get_cfg(nopause=False):
cfg = get_inp_cfg()
# outputs
cfg['only_target_discriminative'] = True
cfg['target_domain_name'] = 'segmentsemantic'
cfg['return_accuracy'] = True
cfg['target_from_filenames'] = load_ops.semantic_segment_rebalanced
# outputs
cfg['target_num_channels'] = 17
cfg['target_dim'] = (256, 256) # (1024, 1024)
cfg['target_from_filenames_kwargs'] = {
'new_dims': (256, 256),
'domain': 'segmentsemantic'
}
cfg['mask_by_target_func'] = True
# masks
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
return cfg
