o oi6@sddlmZmZmZddlZddlmZmZddlm Z m Z ddl m Z m Z mZddlmZmZmZddlmZd efd d Z    dd ed edededeedeeeefdeeeefdeed eeeffddZGdddZdS))OptionalTupleUnionN)Tensor as_tensor)KORNIA_CHECK_LAFKORNIA_CHECK_SHAPE)get_laf_centerget_laf_orientation get_laf_scale) AdalamConfig _no_match adalam_core) dist_matrixreturncCs$tddddddddd d td d S) zReturn default Adalam Config.dg?Tcpu) area_ratiosearch_expansion ransac_iters min_inliersmin_confidence orientation_difference_thresholdscale_rate_thresholddetected_scale_rate_thresholdrefitforce_seed_mnndevice)r torchr$r&r&P/home/ubuntu/.local/lib/python3.10/site-packages/kornia/feature/adalam/adalam.pyget_adalam_default_config"sr(desc1desc2lafs1lafs2confighw1hw2dmc Cst|ddgt|ddgt|t|t}|dur#|j|d<n&t}|D]\} } | |vrDtd| dt|dq*| || <q*t|} | j t | dd t | dd ||||t | dt | dt | dt | dd d \} } | | fS) aPerform 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, B2, 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)`. hw1: Height/width of image. hw2: Height/width of image. 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. BDIMNr$zFWARNING: custom configuration contains a key which is not recognized (z). Known configurations are .T) return_dist)rrr(r$itemskeysprintlist AdalamFiltermatch_and_filterr reshaper r )r)r*r+r,r-r.r/r0config_keyval adalam_objectidxsqualityr&r&r' match_adalam3s@"          rDc@s2eZdZddeeddfddZ        ddeded ed ed eed eeeefd eeeefdeedeedeedeede de eeefeffddZ       ddedededed eeeefd eeeefdeedeedeedeede de eeefeffddZ dS)r;N custom_configrcCs|dur ||_dSt|_dS)a3Wrap 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. N)r-r()selfrEr&r&r'__init__ys  zAdalamFilter.__init__Fk1k2putative_matchesscoresmnnim1shapeim2shapeo1o2s1s2r6c CsPtt||||||||| | | |j| d WdS1s!wYdS)a Call the core functionality of AdaLAM, i.e. just outlier filtering. No sanity check is performed on the inputs. Args: 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: 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,) o2: same as o1 but for destination. s1: 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,) s2: same as s1 but for destination. return_dist: if True, inverse confidence value is also outputted. Returns: Filtered putative matches. A long tensor with shape (num_filtered_matches, 2) with indices of corresponding keypoints in k1 and k2. ) fnn12scores1rLrMrNrOrPrQrRr-r6N)r%no_gradrr-) rFrHrIrJrKrLrMrNrOrPrQrRr6r&r&r'filter_matchess" 7$zAdalamFilter.filter_matchesd1d2c Cs| dus| dur|jddurtd|dus|dur&|jddur&tdt||jdtjd} t||jdtjd} t||jdtjd}t||jdtjd}|durmt||jdtjd}t||jdtjd}n||}}| durt| |jdtjd}t| |jdtjd}n| | }}t|dkst|dkrt|\}}| r||fS|St||d d }tj|d dd d \}}|ddd f}|ddd f|dddf d}|jdrtj |d d\}}||tj | j d |jddk}nd}| | | |||||||||| S)a Match and filter 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. Args: 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: 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,) o2: Same as o1 for destination. s1: 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,) s2: Same as s1 for destination. return_dist: if True, inverse confidence value is also outputted. Returns: Filtered putative matches. A long tensor with shape (num_filtered_matches, 2) with indices of corresponding keypoints in k1 and k2. Nr zCurrent configuration considers keypoint scales for filtering, but scales have not been provided. Please either provide scales or set 'scale_rate_threshold' to None to disable scale filteringrzCurrent configuration considers keypoint orientations for filtering, but orientations have not been provided. Please either provide orientations or set 'orientation_difference_threshold' to None to disable orientations filteringr$)r$dtyper F) is_normalizedr5)kdimlargestrgMbP?r#)r\)r$)r-AttributeErrorrr%float32lenrrtopk clamp_min_minarangeshaperV)rFrHrIrWrXrMrNrOrPrQrRr6_k1_k2_d1_d2_o1_o2_s1_s2rBdistsdistmatdd12nn12rJrK_dd21nn21rLr&r&r'r<sL6   & $zAdalamFilter.match_and_filter)N)NNNNNNNF)NNNNNNF) __name__ __module__ __qualname__rr rGrrintboolrrVr<r&r&r&r'r;xs      N     r;)NNNN)typingrrrr% kornia.corerrkornia.core.checkrrkornia.feature.lafr r r corer rrutilsrr(rwrDr;r&r&r&r's@  E