Source code for kornia.feature.scale_space_detector

from typing import Tuple, List, Optional

import torch
import torch.nn as nn
import torch.nn.functional as F

from kornia.geometry import angle_to_rotation_matrix
from kornia.feature.responses import BlobHessian
from kornia.geometry import ConvSoftArgmax3d
from kornia.feature.orientation import PassLAF
from kornia.feature.laf import (
    denormalize_laf, scale_laf,
    normalize_laf, laf_is_inside_image)
from kornia.geometry.transform import ScalePyramid


def _scale_index_to_scale(max_coords: torch.Tensor, sigmas: torch.Tensor, num_levels: int) -> torch.Tensor:
    """Auxiliary function for ScaleSpaceDetector. Converts scale level index from ConvSoftArgmax3d
    to the actual scale, using the sigmas from the ScalePyramid output
    Args:
        max_coords: (torch.Tensor): tensor [BxNx3].
        sigmas: (torch.Tensor): tensor [BxNxD], D >= 1

    Returns:
        torch.Tensor:  tensor [BxNx3].
    """
    # depth (scale) in coord_max is represented as (float) index, not the scale yet.
    # we will interpolate the scale using pytorch.grid_sample function
    # Because grid_sample is for 4d input only, we will create fake 2nd dimension
    # ToDo: replace with 3d input, when grid_sample will start to support it

    # Reshape for grid shape
    B, N, _ = max_coords.shape
    L: int = sigmas.size(1)
    scale_coords = max_coords[:, :, 0].contiguous().view(-1, 1, 1, 1)
    # Replace the scale_x_y
    out = torch.cat([sigmas[0, 0] * torch.pow(2.0, scale_coords / float(num_levels)).view(B, N, 1),
                     max_coords[:, :, 1:]], dim=2)
    return out


def _create_octave_mask(mask: torch.Tensor, octave_shape: List[int]) -> torch.Tensor:
    """Downsamples a mask based on the given octave shape."""
    mask_shape = octave_shape[-2:]
    mask_octave = F.interpolate(mask, mask_shape, mode='bilinear', align_corners=False)  # type: ignore
    return mask_octave.unsqueeze(1)


[docs]class ScaleSpaceDetector(nn.Module): """Module for differentiable local feature detection, as close as possible to classical local feature detectors like Harris, Hessian-Affine or SIFT (DoG). It has 5 modules inside: scale pyramid generator, response ("cornerness") function, soft nms function, affine shape estimator and patch orientation estimator. Each of those modules could be replaced with learned custom one, as long, as they respect output shape. Args: num_features: (int) Number of features to detect. default = 500. In order to keep everything batchable, output would always have num_features output, even for completely homogeneous images. mr_size: (float), default 6.0. Multiplier for local feature scale compared to the detection scale. 6.0 is matching OpenCV 12.0 convention for SIFT. scale_pyr_module: (nn.Module), which generates scale pyramid. See :class:`~kornia.geometry.ScalePyramid` for details. Default is ScalePyramid(3, 1.6, 10) resp_module: (nn.Module), which calculates 'cornerness' of the pixel. Default is BlobHessian(). nms_module: (nn.Module), which outputs per-patch coordinates of the response maxima. See :class:`~kornia.geometry.ConvSoftArgmax3d` for details. ori_module: (nn.Module) for local feature orientation estimation. Default is :class:`~kornia.feature.PassLAF`, which does nothing. See :class:`~kornia.feature.LAFOrienter` for details. aff_module: (nn.Module) for local feature affine shape estimation. Default is :class:`~kornia.feature.PassLAF`, which does nothing. See :class:`~kornia.feature.LAFAffineShapeEstimator` for details. minima_are_also_good: (bool) if True, then both response function minima and maxima are detected Useful for symmetric response functions like DoG or Hessian. Default is False """ def __init__(self, num_features: int = 500, mr_size: float = 6.0, scale_pyr_module: nn.Module = ScalePyramid(3, 1.6, 15), resp_module: nn.Module = BlobHessian(), nms_module: nn.Module = ConvSoftArgmax3d((3, 3, 3), (1, 1, 1), (1, 1, 1), normalized_coordinates=False, output_value=True), ori_module: nn.Module = PassLAF(), aff_module: nn.Module = PassLAF(), minima_are_also_good: bool = False, scale_space_response=False): super(ScaleSpaceDetector, self).__init__() self.mr_size = mr_size self.num_features = num_features self.scale_pyr = scale_pyr_module self.resp = resp_module self.nms = nms_module self.ori = ori_module self.aff = aff_module self.minima_are_also_good = minima_are_also_good # scale_space_response should be True if the response function works on scale space # like Difference-of-Gaussians self.scale_space_response = scale_space_response return def __repr__(self): return self.__class__.__name__ + '('\ 'num_features=' + str(self.num_features) + ', ' + \ 'mr_size=' + str(self.mr_size) + ', ' + \ 'scale_pyr=' + self.scale_pyr.__repr__() + ', ' + \ 'resp=' + self.resp.__repr__() + ', ' + \ 'nms=' + self.nms.__repr__() + ', ' + \ 'ori=' + self.ori.__repr__() + ', ' + \ 'aff=' + self.aff.__repr__() + ')' def detect(self, img: torch.Tensor, num_feats: int, mask: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]: dev: torch.device = img.device dtype: torch.dtype = img.dtype sp, sigmas, pix_dists = self.scale_pyr(img) all_responses = [] all_lafs = [] for oct_idx, octave in enumerate(sp): sigmas_oct = sigmas[oct_idx] pix_dists_oct = pix_dists[oct_idx] B, CH, L, H, W = octave.size() # Run response function if self.scale_space_response: oct_resp = self.resp(octave, sigmas_oct.view(-1)) else: oct_resp = self.resp(octave.permute(0, 2, 1, 3, 4).reshape(B * L, CH, H, W), sigmas_oct.view(-1)).view(B, L, CH, H, W) # We want nms for scale responses, so reorder to (B, CH, L, H, W) oct_resp = oct_resp.permute(0, 2, 1, 3, 4) # 3rd extra level is required for DoG only if self.scale_pyr.extra_levels % 2 != 0: # type: ignore oct_resp = oct_resp[:, :, :-1] if mask is not None: oct_mask: torch.Tensor = _create_octave_mask(mask, oct_resp.shape) oct_resp = oct_mask * oct_resp # Differentiable nms coord_max, response_max = self.nms(oct_resp) if self.minima_are_also_good: coord_min, response_min = self.nms(-oct_resp) take_min_mask = (response_min > response_max).to(response_max.dtype) response_max = response_min * take_min_mask + (1 - take_min_mask) * response_max coord_max = coord_min * take_min_mask.unsqueeze(2) + (1 - take_min_mask.unsqueeze(2)) * coord_max # Now, lets crop out some small responses responses_flatten = response_max.view(response_max.size(0), -1) # [B, N] max_coords_flatten = coord_max.view(response_max.size(0), 3, -1).permute(0, 2, 1) # [B, N, 3] if responses_flatten.size(1) > num_feats: resp_flat_best, idxs = torch.topk(responses_flatten, k=num_feats, dim=1) max_coords_best = torch.gather(max_coords_flatten, 1, idxs.unsqueeze(-1).repeat(1, 1, 3)) else: resp_flat_best = responses_flatten max_coords_best = max_coords_flatten B, N = resp_flat_best.size() # Converts scale level index from ConvSoftArgmax3d to the actual scale, using the sigmas max_coords_best = _scale_index_to_scale(max_coords_best, sigmas_oct, self.scale_pyr.n_levels) # type: ignore # Create local affine frames (LAFs) rotmat = torch.eye(2, dtype=dtype, device=dev).view(1, 1, 2, 2) current_lafs = torch.cat([self.mr_size * max_coords_best[:, :, 0].view(B, N, 1, 1) * rotmat, max_coords_best[:, :, 1:3].view(B, N, 2, 1)], dim=3) # Zero response lafs, which touch the boundary good_mask = laf_is_inside_image(current_lafs, octave[:, 0]) resp_flat_best = resp_flat_best * good_mask.to(dev, dtype) # Normalize LAFs current_lafs = normalize_laf(current_lafs, octave[:, 0]) # We don`t need # of scale levels, only shape all_responses.append(resp_flat_best) all_lafs.append(current_lafs) # Sort and keep best n responses: torch.Tensor = torch.cat(all_responses, dim=1) lafs: torch.Tensor = torch.cat(all_lafs, dim=1) responses, idxs = torch.topk(responses, k=num_feats, dim=1) lafs = torch.gather(lafs, 1, idxs.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, 2, 3)) return responses, denormalize_laf(lafs, img)
[docs] def forward(self, img: torch.Tensor, # type: ignore mask: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]: # type: ignore """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 (torch.Tensor): image to extract features with shape [BxCxHxW] mask (torch.Tensor, optional): a mask with weights where to apply the response function. The shape must be the same as the input image. Returns: lafs (torch.Tensor): shape [BxNx2x3]. Detected local affine frames. responses (torch.Tensor): shape [BxNx1]. Response function values for corresponding lafs""" responses, lafs = self.detect(img, self.num_features, mask) lafs = self.aff(lafs, img) lafs = self.ori(lafs, img) return lafs, responses