Source code for kornia.feature.integrated

from typing import Dict, List, Optional, Tuple

import torch
import torch.nn as nn

from kornia.color import rgb_to_grayscale
from kornia.geometry.subpix import ConvQuadInterp3d
from kornia.geometry.transform import ScalePyramid

from .affine_shape import LAFAffNetShapeEstimator
from .hardnet import HardNet
from .laf import extract_patches_from_pyramid, get_laf_center, raise_error_if_laf_is_not_valid
from .orientation import LAFOrienter, PassLAF
from .responses import BlobDoG, CornerGFTT
from .scale_space_detector import ScaleSpaceDetector
from .siftdesc import SIFTDescriptor


[docs]def get_laf_descriptors(img: torch.Tensor, lafs: torch.Tensor, patch_descriptor: nn.Module, patch_size: int = 32, grayscale_descriptor: bool = True) -> torch.Tensor: r"""Function to get local descriptors, corresponding to LAFs (keypoints). Args: img: image features with shape :math:`(B,C,H,W)`. lafs: local affine frames :math:`(B,N,2,3)`. patch_descriptor: patch descriptor module, e.g. :class:`~kornia.feature.SIFTDescriptor` or :class:`~kornia.feature.HardNet`. patch_size: patch size in pixels, which descriptor expects. grayscale_descriptor: True if ``patch_descriptor`` expects single-channel image. Returns: Local descriptors of shape :math:`(B,N,D)` where :math:`D` is descriptor size. """ raise_error_if_laf_is_not_valid(lafs) patch_descriptor = patch_descriptor.to(img) patch_descriptor.eval() timg: torch.Tensor = img if grayscale_descriptor and img.size(1) == 3: timg = rgb_to_grayscale(img) patches: torch.Tensor = extract_patches_from_pyramid(timg, lafs, patch_size) # Descriptor accepts standard tensor [B, CH, H, W], while patches are [B, N, CH, H, W] shape # So we need to reshape a bit :) B, N, CH, H, W = patches.size() return patch_descriptor(patches.view(B * N, CH, H, W)).view(B, N, -1)
[docs]class LAFDescriptor(nn.Module): r"""Module to get local descriptors, corresponding to LAFs (keypoints). Internally uses :func:`~kornia.feature.get_laf_descriptors`. Args: patch_descriptor_module: patch descriptor module, e.g. :class:`~kornia.feature.SIFTDescriptor` or :class:`~kornia.feature.HardNet`. Default: :class:`~kornia.feature.HardNet`. patch_size: patch size in pixels, which descriptor expects. grayscale_descriptor: ``True`` if patch_descriptor expects single-channel image. """ def __init__(self, patch_descriptor_module: Optional[nn.Module] = None, patch_size: int = 32, grayscale_descriptor: bool = True) -> None: super().__init__() if patch_descriptor_module is None: patch_descriptor_module = HardNet(True) self.descriptor = patch_descriptor_module self.patch_size = patch_size self.grayscale_descriptor = grayscale_descriptor def __repr__(self) -> str: return self.__class__.__name__ + '(' + \ 'descriptor=' + self.descriptor.__repr__() + ', ' + \ 'patch_size=' + str(self.patch_size) + ', ' + \ 'grayscale_descriptor=' + str(self.grayscale_descriptor) + ')'
[docs] def forward(self, img: torch.Tensor, lafs: torch.Tensor) -> torch.Tensor: r"""Three stage local feature detection. First the location and scale of interest points are determined by detect function. Then affine shape and orientation. Args: img: image features with shape :math:`(B,C,H,W)`. lafs: local affine frames :math:`(B,N,2,3)`. Returns: Local descriptors of shape :math:`(B,N,D)` where :math:`D` is descriptor size. """ return get_laf_descriptors(img, lafs, self.descriptor, self.patch_size, self.grayscale_descriptor)
[docs]class LocalFeature(nn.Module): """Module, which combines local feature detector and descriptor. Args: detector: the detection module. descriptor: the descriptor module. """ def __init__(self, detector: ScaleSpaceDetector, descriptor: LAFDescriptor) -> None: super().__init__() self.detector = detector self.descriptor = descriptor
[docs] def forward(self, img: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # type: ignore """ Args: img: image to extract features with shape :math:`(B,C,H,W)`. mask: a mask with weights where to apply the response function. The shape must be the same as the input image. Returns: - Detected local affine frames with shape :math:`(B,N,2,3)`. - Response function values for corresponding lafs with shape :math:`(B,N,1)`. - Local descriptors of shape :math:`(B,N,D)` where :math:`D` is descriptor size. """ lafs, responses = self.detector(img, mask) descs = self.descriptor(img, lafs) return (lafs, responses, descs)
[docs]class SIFTFeature(LocalFeature): """Convenience module, which implements DoG detector + (Root)SIFT descriptor. Still not as good as OpenCV/VLFeat because of https://github.com/kornia/kornia/pull/884, but we are working on it """ def __init__(self, num_features: int = 8000, upright: bool = False, rootsift: bool = True, device: torch.device = torch.device('cpu')): patch_size: int = 41 detector = ScaleSpaceDetector(num_features, resp_module=BlobDoG(), nms_module=ConvQuadInterp3d(10), scale_pyr_module=ScalePyramid(3, 1.6, 32, double_image=True), ori_module=PassLAF() if upright else LAFOrienter(19), scale_space_response=True, minima_are_also_good=True, mr_size=6.0).to(device) descriptor = LAFDescriptor(SIFTDescriptor(patch_size=patch_size, rootsift=rootsift), patch_size=patch_size, grayscale_descriptor=True).to(device) super().__init__(detector, descriptor)
[docs]class GFTTAffNetHardNet(LocalFeature): """Convenience module, which implements GFTT detector + AffNet-HardNet descriptor.""" def __init__(self, num_features: int = 8000, upright: bool = False, device: torch.device = torch.device('cpu')): detector = ScaleSpaceDetector(num_features, resp_module=CornerGFTT(), nms_module=ConvQuadInterp3d(10, 1e-5), scale_pyr_module=ScalePyramid(3, 1.6, 32, double_image=False), ori_module=PassLAF() if upright else LAFOrienter(19), aff_module=LAFAffNetShapeEstimator(True).eval(), mr_size=6.0).to(device) descriptor = LAFDescriptor(None, patch_size=32, grayscale_descriptor=True).to(device) super().__init__(detector, descriptor)
[docs]class LocalFeatureMatcher(nn.Module): r"""Module, which finds correspondences between two images based on local features. Args: local_feature: Local feature detector. See :class:`~kornia.feature.GFTTAffNetHardNet`. matcher: Descriptor matcher, see :class:`~kornia.feature.DescriptorMatcher`. Returns: Dict[str, torch.Tensor]: Dictionary with image correspondences and confidence scores. Example: >>> img1 = torch.rand(1, 1, 320, 200) >>> img2 = torch.rand(1, 1, 128, 128) >>> input = {"image0": img1, "image1": img2} >>> gftt_hardnet_matcher = LocalFeatureMatcher( ... GFTTAffNetHardNet(10), kornia.feature.DescriptorMatcher('snn', 0.8) ... ) >>> out = gftt_hardnet_matcher(input) """ def __init__(self, local_feature: nn.Module, matcher: nn.Module) -> None: super().__init__() self.local_feature = local_feature self.matcher = matcher self.eval() def extract_features(self, image: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]: """Function for feature extraction from simple image.""" lafs0, resps0, descs0 = self.local_feature(image, mask) return {"lafs": lafs0, "responses": resps0, "descriptors": descs0} def no_match_output(self, device: torch.device, dtype: torch.dtype) -> dict: return { 'keypoints0': torch.empty(0, 2, device=device, dtype=dtype), 'keypoints1': torch.empty(0, 2, device=device, dtype=dtype), 'lafs0': torch.empty(0, 0, 2, 3, device=device, dtype=dtype), 'lafs1': torch.empty(0, 0, 2, 3, device=device, dtype=dtype), 'confidence': torch.empty(0, device=device, dtype=dtype), 'batch_indexes': torch.empty(0, device=device, dtype=torch.long) }
[docs] def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: """ Args: data: dictionary containing the input data in the following format: Keyword Args: image0: left image with shape :math:`(N, 1, H1, W1)`. image1: right image with shape :math:`(N, 1, H2, W2)`. mask0 (optional): left image mask. '0' indicates a padded position :math:`(N, H1, W1)`. mask1 (optional): right image mask. '0' indicates a padded position :math:`(N, H2, W2)`. Returns: - ``keypoints0``, matching keypoints from image0 :math:`(NC, 2)`. - ``keypoints1``, matching keypoints from image1 :math:`(NC, 2)`. - ``confidence``, confidence score [0, 1] :math:`(NC)`. - ``lafs0``, matching LAFs from image0 :math:`(1, NC, 2, 3)`. - ``lafs1``, matching LAFs from image1 :math:`(1, NC, 2, 3)`. - ``batch_indexes``, batch indexes for the keypoints and lafs :math:`(NC)`. """ num_image_pairs: int = data['image0'].shape[0] if ('lafs0' not in data.keys()) or ('descriptors0' not in data.keys()): # One can supply pre-extracted local features feats_dict0: Dict[str, torch.Tensor] = self.extract_features(data['image0']) lafs0, descs0 = feats_dict0['lafs'], feats_dict0['descriptors'] else: lafs0, descs0 = data['lafs0'], data['descriptors0'] if ('lafs1' not in data.keys()) or ('descriptors1' not in data.keys()): feats_dict1: Dict[str, torch.Tensor] = self.extract_features(data['image1']) lafs1, descs1 = feats_dict1['lafs'], feats_dict1['descriptors'] else: lafs1, descs1 = data['lafs1'], data['descriptors1'] keypoints0: torch.Tensor = get_laf_center(lafs0) keypoints1: torch.Tensor = get_laf_center(lafs1) out_keypoints0: List[torch.Tensor] = [] out_keypoints1: List[torch.Tensor] = [] out_confidence: List[torch.Tensor] = [] out_batch_indexes: List[torch.Tensor] = [] out_lafs0: List[torch.Tensor] = [] out_lafs1: List[torch.Tensor] = [] for batch_idx in range(num_image_pairs): dists, idxs = self.matcher(descs0[batch_idx], descs1[batch_idx]) if len(idxs) == 0: continue current_keypoints_0 = keypoints0[batch_idx, idxs[:, 0]] current_keypoints_1 = keypoints1[batch_idx, idxs[:, 1]] current_lafs_0 = lafs0[batch_idx, idxs[:, 0]] current_lafs_1 = lafs1[batch_idx, idxs[:, 1]] out_confidence.append(1.0 - dists) batch_idxs = batch_idx * torch.ones(len(dists), device=keypoints0.device, dtype=torch.long) out_keypoints0.append(current_keypoints_0) out_keypoints1.append(current_keypoints_1) out_lafs0.append(current_lafs_0) out_lafs1.append(current_lafs_1) out_batch_indexes.append(batch_idxs) if len(out_batch_indexes) == 0: return self.no_match_output(data['image0'].device, data['image0'].dtype) return { 'keypoints0': torch.cat(out_keypoints0, dim=0).view(-1, 2), 'keypoints1': torch.cat(out_keypoints1, dim=0).view(-1, 2), 'lafs0': torch.cat(out_lafs0, dim=0).view(1, -1, 2, 3), 'lafs1': torch.cat(out_lafs1, dim=0).view(1, -1, 2, 3), 'confidence': torch.cat(out_confidence, dim=0).view(-1), 'batch_indexes': torch.cat(out_batch_indexes, dim=0).view(-1) }