# Integrated from original AdaLAM repo
# https://github.com/cavalli1234/AdaLAM
# Copyright (c) 2020, Luca Cavalli
from typing import Dict, Optional, Tuple
import torch
from torch import Tensor
from kornia.feature.laf import get_laf_center, get_laf_orientation, get_laf_scale
from kornia.testing import KORNIA_CHECK_LAF, KORNIA_CHECK_SHAPE
from kornia.utils.helpers import get_cuda_device_if_available
from .core import adalam_core
from .utils import dist_matrix
def get_adalam_default_config():
DEFAULT_CONFIG = {
'area_ratio': 100, # Ratio between seed circle area and image area. Higher values produce more seeds with smaller neighborhoods. # noqa: E501
'search_expansion': 4, # Expansion factor of the seed circle radius for the purpose of collecting neighborhoods. Increases neighborhood radius without changing seed distribution # noqa: E501
'ransac_iters': 128, # Fixed number of inner GPU-RANSAC iterations
'min_inliers': 6, # Minimum number of inliers required to accept inliers coming from a neighborhood # noqa: E501
'min_confidence': 200, # Threshold used by the confidence-based GPU-RANSAC
'orientation_difference_threshold': 30, # Maximum difference in orientations for a point to be accepted in a neighborhood. Set to None to disable the use of keypoint orientations. # noqa: E501
'scale_rate_threshold': 1.5, # Maximum difference (ratio) in scales for a point to be accepted in a neighborhood. Set to None to disable the use of keypoint scales. # noqa: E501
'detected_scale_rate_threshold': 5, # Prior on maximum possible scale change detectable in image couples. Affinities with higher scale changes are regarded as outliers. # noqa: E501
'refit': True, # Whether to perform refitting at the end of the RANSACs. Generally improves accuracy at the cost of runtime. # noqa: E501
'force_seed_mnn': True, # Whether to consider only MNN for the purpose of selecting seeds. Generally improves accuracy at the cost of runtime. # noqa: E501
# You can provide a MNN mask in input to skip MNN computation and still get the improvement.
'device': get_cuda_device_if_available(), # Device to be used for running AdaLAM. Use GPU if available. # noqa: E501
}
return DEFAULT_CONFIG
[docs]def match_adalam(
desc1: Tensor,
desc2: Tensor,
lafs1: Tensor,
lafs2: Tensor,
config: Optional[Dict] = None,
hw1: Optional[Tensor] = None,
hw2: Optional[Tensor] = None,
dm: Optional[Tensor] = None,
) -> Tuple[Tensor, Tensor]:
"""Function, which performs descriptor matching, followed by AdaLAM filtering (see :cite:`AdaLAM2020` for more
details)
If the distance matrix dm is not provided, :py:func:`torch.cdist` is used.
Args:
desc1: Batch of descriptors of a shape :math:`(B1, D)`.
desc2: Batch of descriptors of a shape :math:`(B2, D)`.
lafs1: LAFs of a shape :math:`(1, B1, 2, 3)`.
lafs2: LAFs of a shape :math:`(1, B1, 2, 3)`.
config: dict with AdaLAM config
dm: Tensor containing the distances from each descriptor in desc1
to each descriptor in desc2, shape of :math:`(B1, B2)`.
Return:
- Descriptor distance of matching descriptors, shape of :math:`(B3, 1)`.
- Long tensor indexes of matching descriptors in desc1 and desc2. Shape: :math:`(B3, 2)`,
where 0 <= B3 <= B1.
"""
KORNIA_CHECK_SHAPE(desc1, ["B", "DIM"])
KORNIA_CHECK_SHAPE(desc2, ["B", "DIM"])
KORNIA_CHECK_LAF(lafs1)
KORNIA_CHECK_LAF(lafs2)
if config is None:
config_ = get_adalam_default_config()
config_['device'] = desc1.device
else:
config_ = config
adalam_object = AdalamFilter(config_)
idxs = adalam_object.match_and_filter(
get_laf_center(lafs1).reshape(-1, 2),
get_laf_center(lafs2).reshape(-1, 2),
desc1,
desc2,
hw1,
hw2,
get_laf_orientation(lafs1).reshape(-1),
get_laf_orientation(lafs2).reshape(-1),
get_laf_scale(lafs1).reshape(-1),
get_laf_scale(lafs2).reshape(-1),
)
quality = torch.ones(len(idxs), 1, dtype=desc1.dtype, device=desc1.device)
return quality, idxs
class AdalamFilter:
DEFAULT_CONFIG = get_adalam_default_config()
def __init__(self, custom_config: dict = None):
"""This class acts as a wrapper to the method AdaLAM for outlier filtering.
init args:
custom_config: dictionary overriding the default configuration. Missing parameters are kept as default.
See documentation of DEFAULT_CONFIG for specific explanations on the accepted parameters.
"""
self.config = AdalamFilter.DEFAULT_CONFIG.copy()
if custom_config is not None:
for key, val in custom_config.items():
if key not in self.config.keys():
print(
"WARNING: custom configuration contains a key which is not recognized ({key}). "
"Known configurations are {list(self.config.keys())}."
)
continue
self.config[key] = val
def filter_matches(
self,
k1: torch.Tensor,
k2: torch.Tensor,
putative_matches: torch.Tensor,
scores: torch.Tensor,
mnn: torch.Tensor = None,
im1shape: tuple = None,
im2shape: tuple = None,
o1: torch.Tensor = None,
o2: torch.Tensor = None,
s1: torch.Tensor = None,
s2: torch.Tensor = None,
):
"""Call the core functionality of AdaLAM, i.e. just outlier filtering. No sanity check is performed on the
inputs.
Inputs:
k1: keypoint locations in the source image, in pixel coordinates.
Expected a float32 tensor with shape (num_keypoints_in_source_image, 2).
k2: keypoint locations in the destination image, in pixel coordinates.
Expected a float32 tensor with shape (num_keypoints_in_destination_image, 2).
putative_matches: Initial set of putative matches to be filtered.
The current implementation assumes that these are unfiltered nearest neighbor matches,
so it requires this to be a list of indices a_i such that the source keypoint i is associated to the destination keypoint a_i.
For now to use AdaLAM on different inputs a workaround on the input format is required.
Expected a long tensor with shape (num_keypoints_in_source_image,).
scores: Confidence scores on the putative_matches. Usually holds Lowe's ratio scores.
mnn: A mask indicating which putative matches are also mutual nearest neighbors. See documentation on 'force_seed_mnn' in the DEFAULT_CONFIG.
If None, it disables the mutual nearest neighbor filtering on seed point selection.
Expected a bool tensor with shape (num_keypoints_in_source_image,)
im1shape: Shape of the source image. If None, it is inferred from keypoints max and min, at the cost of wasted runtime. So please provide it.
Expected a tuple with (width, height) or (height, width) of source image
im2shape: Shape of the destination image. If None, it is inferred from keypoints max and min, at the cost of wasted runtime. So please provide it.
Expected a tuple with (width, height) or (height, width) of destination image
o1/o2: keypoint orientations in degrees. They can be None if 'orientation_difference_threshold' in config is set to None.
See documentation on 'orientation_difference_threshold' in the DEFAULT_CONFIG.
Expected a float32 tensor with shape (num_keypoints_in_source/destination_image,)
s1/s2: keypoint scales. They can be None if 'scale_rate_threshold' in config is set to None.
See documentation on 'scale_rate_threshold' in the DEFAULT_CONFIG.
Expected a float32 tensor with shape (num_keypoints_in_source/destination_image,)
Returns:
Filtered putative matches.
A long tensor with shape (num_filtered_matches, 2) with indices of corresponding keypoints in k1 and k2.
""" # noqa: E501
with torch.no_grad():
return adalam_core(
k1,
k2,
fnn12=putative_matches,
scores1=scores,
mnn=mnn,
im1shape=im1shape,
im2shape=im2shape,
o1=o1,
o2=o2,
s1=s1,
s2=s2,
config=self.config,
)
def match_and_filter(self, k1, k2, d1, d2, im1shape=None, im2shape=None, o1=None, o2=None, s1=None, s2=None):
"""Standard matching and filtering with AdaLAM. This function:
- performs some elementary sanity check on the inputs;
- wraps input arrays into torch tensors and loads to GPU if necessary;
- extracts nearest neighbors;
- finds mutual nearest neighbors if required;
- finally calls AdaLAM filtering.
Inputs:
k1: keypoint locations in the source image, in pixel coordinates.
Expected an array with shape (num_keypoints_in_source_image, 2).
k2: keypoint locations in the destination image, in pixel coordinates.
Expected an array with shape (num_keypoints_in_destination_image, 2).
d1: descriptors in the source image.
Expected an array with shape (num_keypoints_in_source_image, descriptor_size).
d2: descriptors in the destination image.
Expected an array with shape (num_keypoints_in_destination_image, descriptor_size).
im1shape: Shape of the source image. If None, it is inferred from keypoints max and min, at the cost of wasted runtime. So please provide it.
Expected a tuple with (width, height) or (height, width) of source image
im2shape: Shape of the destination image. If None, it is inferred from keypoints max and min, at the cost of wasted runtime. So please provide it.
Expected a tuple with (width, height) or (height, width) of destination image
o1/o2: keypoint orientations in degrees. They can be None if 'orientation_difference_threshold' in config is set to None.
See documentation on 'orientation_difference_threshold' in the DEFAULT_CONFIG.
Expected an array with shape (num_keypoints_in_source/destination_image,)
s1/s2: keypoint scales. They can be None if 'scale_rate_threshold' in config is set to None.
See documentation on 'scale_rate_threshold' in the DEFAULT_CONFIG.
Expected an array with shape (num_keypoints_in_source/destination_image,)
Returns:
Filtered putative matches.
A long tensor with shape (num_filtered_matches, 2) with indices of corresponding keypoints in k1 and k2.
""" # noqa: E501
if s1 is None or s2 is None:
if self.config['scale_rate_threshold'] is not None:
raise AttributeError(
"Current configuration considers keypoint scales for filtering, but scales have not been provided.\n" # noqa: E501
"Please either provide scales or set 'scale_rate_threshold' to None to disable scale filtering"
)
if o1 is None or o2 is None:
if self.config['orientation_difference_threshold'] is not None:
raise AttributeError(
"Current configuration considers keypoint orientations for filtering, but orientations have not been provided.\n" # noqa: E501
"Please either provide orientations or set 'orientation_difference_threshold' to None to disable orientations filtering" # noqa: E501
)
k1, k2, d1, d2, o1, o2, s1, s2 = self.__to_torch(k1, k2, d1, d2, o1, o2, s1, s2)
distmat = dist_matrix(d1, d2, is_normalized=False)
dd12, nn12 = torch.topk(distmat, k=2, dim=1, largest=False) # (n1, 2)
putative_matches = nn12[:, 0]
scores = dd12[:, 0] / dd12[:, 1].clamp_min_(1e-3)
if self.config['force_seed_mnn']:
dd21, nn21 = torch.min(distmat, dim=0) # (n2,)
mnn = nn21[putative_matches] == torch.arange(k1.shape[0], device=self.config['device'])
else:
mnn = None
return self.filter_matches(k1, k2, putative_matches, scores, mnn, im1shape, im2shape, o1, o2, s1, s2)
def __to_torch(self, *args):
return (
a if a is None or torch.is_tensor(a) else torch.tensor(a, device=self.config['device'], dtype=torch.float32)
for a in args
)