def __init__(self,
use_cuda=False,
border=16,
num_features=500,
patch_size=32,
mrSize=3.0,
nlevels=8,
num_Baum_iters=0,
init_sigma=1.6):
super(HessianAffinePatchExtractor, self).__init__()
self.mrSize = mrSize
self.PS = patch_size
self.b = border
self.num = num_features
self.use_cuda = use_cuda
self.nlevels = nlevels
self.num_Baum_iters = num_Baum_iters
self.init_sigma = init_sigma
self.Hes = HessianResp()
self.OriDet = OrientationDetector(patch_size=19)
self.AffShape = AffineShapeEstimator(patch_size=19)
self.ScalePyrGen = ScalePyramid(nScales=self.nlevels,
init_sigma=self.init_sigma,
border=self.b)
return
def __init__(self,
border = 16,
num_features = 500,
patch_size = 32,
mrSize = 3.0,
nlevels = 3,
num_Baum_iters = 0,
init_sigma = 1.6,
RespNet = None, OriNet = None, AffNet = None):
super(ScaleSpaceAffinePatchExtractor, self).__init__()
self.mrSize = mrSize
self.PS = patch_size
self.b = border;
self.num = num_features
self.nlevels = nlevels
self.num_Baum_iters = num_Baum_iters
self.init_sigma = init_sigma
if RespNet is not None:
self.RespNet = RespNet
else:
self.RespNet = HessianResp()
if OriNet is not None:
self.OriNet = OriNet
else:
self.OriNet= OrientationDetector(patch_size = 19);
if AffNet is not None:
self.AffNet = AffNet
else:
self.AffNet = AffineShapeEstimator(patch_size = 19)
self.ScalePyrGen = ScalePyramid(nLevels = self.nlevels, init_sigma = self.init_sigma, border = self.b)
return
def __init__(self,
descriptors_list,
names_list,
loss_fn=loss_HardNegC,
cuda=False,
lr=1e-0,
loss_name='HardNegC'):
assert len(descriptors_list) == len(names_list)
self.desc_list = descriptors_list
self.names_list = names_list
self.colors = ['r', 'g', 'b', 'y', 'm', 'k', 'c']
self.cuda = cuda
self.out_lafs1 = {}
self.out_lafs2 = {}
self.loss_func = loss_fn
self.loss_name = loss_name
self.save_dir = self.loss_name + '_saves'
self.out_loss_mean = {}
self.cent_diff = {}
self.shape_diff = {}
self.snn = {}
self.lr = lr
for dn in names_list:
self.out_lafs1[dn] = []
self.out_lafs2[dn] = []
self.out_loss_mean[dn] = []
self.cent_diff[dn] = []
self.shape_diff[dn] = []
self.snn[dn] = []
self.ScalePyrGen = ScalePyramid(nLevels=1, init_sigma=1.6, border=32)
return
def __init__(self,
border = 16,
num_features = 500,
patch_size = 32,
mrSize = 3.0,
nlevels = 3,
threshold = 0,
num_Baum_iters = 0,
init_sigma = 1.6,
RespNet = None, OriNet = None, AffNet = None):
super(ScaleSpaceAffinePatchExtractor, self).__init__()
self.mrSize = mrSize
self.PS = patch_size
self.b = border;
self.num = num_features
self.th = threshold
if threshold > 0:
print('Threshold is set to ', threshold, 'num_features is ignored')
self.num = -1
else:
print('Threshold is not set, max num_features = ', self.num, ' will be outputed')
self.nlevels = nlevels
self.num_Baum_iters = num_Baum_iters
self.init_sigma = init_sigma
if RespNet is not None:
self.RespNet = RespNet
else:
self.RespNet = HessianResp()
if OriNet is not None:
self.OriNet = OriNet
else:
self.OriNet= OrientationDetector(patch_size = 19);
if AffNet is not None:
self.AffNet = AffNet
else:
self.AffNet = AffineShapeEstimator(patch_size = 19)
self.ScalePyrGen = ScalePyramid(nLevels = self.nlevels, init_sigma = self.init_sigma, border = self.b)
return