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from math import log
from loguru import logger
import torch
import torch.nn.functional as F
from einops import repeat
from kornia.utils import create_meshgrid
from einops.einops import rearrange
from .geometry import warp_kpts, warp_kpts_fine
from kornia.geometry.epipolar import fundamental_from_projections, normalize_transformation
############## ↓ Coarse-Level supervision ↓ ##############
@torch.no_grad()
def mask_pts_at_padded_regions(grid_pt, mask):
"""For megadepth dataset, zero-padding exists in images"""
mask = repeat(mask, 'n h w -> n (h w) c', c=2)
grid_pt[~mask.bool()] = 0
return grid_pt
@torch.no_grad()
def spvs_coarse(data, config):
"""
Update:
data (dict): {
"conf_matrix_gt": [N, hw0, hw1],
'spv_b_ids': [M]
'spv_i_ids': [M]
'spv_j_ids': [M]
'spv_w_pt0_i': [N, hw0, 2], in original image resolution
'spv_pt1_i': [N, hw1, 2], in original image resolution
}
NOTE:
- for scannet dataset, there're 3 kinds of resolution {i, c, f}
- for megadepth dataset, there're 4 kinds of resolution {i, i_resize, c, f}
"""
# 1. misc
device = data['image0'].device
N, _, H0, W0 = data['image0'].shape
_, _, H1, W1 = data['image1'].shape
scale = config['XOFTR']['RESOLUTION'][0]
scale0 = scale * data['scale0'][:, None] if 'scale0' in data else scale
scale1 = scale * data['scale1'][:, None] if 'scale1' in data else scale
h0, w0, h1, w1 = map(lambda x: x // scale, [H0, W0, H1, W1])
# 2. warp grids
# create kpts in meshgrid and resize them to image resolution
grid_pt0_c = create_meshgrid(h0, w0, False, device).reshape(1, h0*w0, 2).repeat(N, 1, 1) # [N, hw, 2]
grid_pt0_i = scale0 * grid_pt0_c
grid_pt1_c = create_meshgrid(h1, w1, False, device).reshape(1, h1*w1, 2).repeat(N, 1, 1)
grid_pt1_i = scale1 * grid_pt1_c
# mask padded region to (0, 0), so no need to manually mask conf_matrix_gt
if 'mask0' in data:
grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data['mask0'])
grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data['mask1'])
# warp kpts bi-directionally and resize them to coarse-level resolution
# (unhandled edge case: points with 0-depth will be warped to the left-up corner)
valid_mask0, w_pt0_i = warp_kpts(grid_pt0_i, data['depth0'], data['depth1'], data['T_0to1'], data['K0'], data['K1'])
valid_mask1, w_pt1_i = warp_kpts(grid_pt1_i, data['depth1'], data['depth0'], data['T_1to0'], data['K1'], data['K0'])
w_pt0_i[~valid_mask0] = 0
w_pt1_i[~valid_mask1] = 0
w_pt0_c = w_pt0_i / scale1
w_pt1_c = w_pt1_i / scale0
# 3. nearest neighbor
w_pt0_c_round = w_pt0_c[:, :, :].round().long()
nearest_index1 = w_pt0_c_round[..., 0] + w_pt0_c_round[..., 1] * w1
w_pt1_c_round = w_pt1_c[:, :, :].round().long()
nearest_index0 = w_pt1_c_round[..., 0] + w_pt1_c_round[..., 1] * w0
# corner case: out of boundary
def out_bound_mask(pt, w, h):
return (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h)
nearest_index1[out_bound_mask(w_pt0_c_round, w1, h1)] = 0
nearest_index0[out_bound_mask(w_pt1_c_round, w0, h0)] = 0
arange_1 = torch.arange(h0*w0, device=device)[None].repeat(N, 1)
arange_0 = torch.arange(h0*w0, device=device)[None].repeat(N, 1)
arange_1[nearest_index1 == 0] = 0
arange_0[nearest_index0 == 0] = 0
arange_b = torch.arange(N, device=device).unsqueeze(1)
# 4. construct a gt conf_matrix
conf_matrix_gt = torch.zeros(N, h0*w0, h1*w1, device=device)
conf_matrix_gt[arange_b, arange_1, nearest_index1] = 1
conf_matrix_gt[arange_b, nearest_index0, arange_0] = 1
conf_matrix_gt[:, 0, 0] = False
b_ids, i_ids, j_ids = conf_matrix_gt.nonzero(as_tuple=True)
data.update({'conf_matrix_gt': conf_matrix_gt})
# 5. save coarse matches(gt) for training fine level
if len(b_ids) == 0:
logger.warning(f"No groundtruth coarse match found for: {data['pair_names']}")
# this won't affect fine-level loss calculation
b_ids = torch.tensor([0], device=device)
i_ids = torch.tensor([0], device=device)
j_ids = torch.tensor([0], device=device)
data.update({
'spv_b_ids': b_ids,
'spv_i_ids': i_ids,
'spv_j_ids': j_ids
})
# 6. save intermediate results (for fast fine-level computation)
data.update({
'spv_w_pt0_i': w_pt0_i,
'spv_pt1_i': grid_pt1_i
})
def compute_supervision_coarse(data, config):
assert len(set(data['dataset_name'])) == 1, "Do not support mixed datasets training!"
data_source = data['dataset_name'][0]
if data_source.lower() in ['scannet', 'megadepth']:
spvs_coarse(data, config)
else:
raise ValueError(f'Unknown data source: {data_source}')
############## ↓ Fine-Level supervision ↓ ##############
def compute_supervision_fine(data, config):
data_source = data['dataset_name'][0]
if data_source.lower() in ['scannet', 'megadepth']:
spvs_fine(data, config)
else:
raise NotImplementedError
@torch.no_grad()
def create_2d_gaussian_kernel(kernel_size, sigma, device):
"""
Create a 2D Gaussian kernel.
Args:
kernel_size (int): Size of the kernel (both width and height).
sigma (float): Standard deviation of the Gaussian distribution.
Returns:
torch.Tensor: 2D Gaussian kernel.
"""
kernel = torch.arange(kernel_size, dtype=torch.float32, device=device) - (kernel_size - 1) / 2
kernel = torch.exp(-kernel**2 / (2 * sigma**2))
kernel = kernel / kernel.sum()
# Outer product to get a 2D kernel
kernel = torch.outer(kernel, kernel)
return kernel
@torch.no_grad()
def create_conf_prob(points, h0, w0, h1, w1, kernel_size = 5, sigma=1):
"""
Place a gaussian kernel in sim matrix for warped points
Args:
data (dict): {
points: (torch.Tensor): (N, L, 2), warped rounded key points
h0, w0, h1, w1: (int), windows sizes
kernel_size: (int), kernel size for the gaussian
sigma: (float), sigma value for gaussian
}
"""
B = points.shape[0]
impulses = torch.zeros(B, h0 * w0, h1, w1, device=points.device)
# Extract the row and column indices
row_indices = points[:, :, 1]
col_indices = points[:, :, 0]
# Set the corresponding locations in the target tensor to 1
impulses[torch.arange(B, device=points.device).view(B, 1, 1),
torch.arange(h0 * w0, device=points.device).view(1, h0 * w0, 1),
row_indices.unsqueeze(-1), col_indices.unsqueeze(-1)] = 1
# mask 0,0 point
impulses[:,:,0,0] = 0
# Create the Gaussian kernel
gaussian_kernel = create_2d_gaussian_kernel(kernel_size, sigma=sigma, device=points.device)
gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size)
# Create distributions at the points
conf_prob = F.conv2d(impulses.view(-1,1,h1,w1), gaussian_kernel, padding=kernel_size//2).view(-1, h0*w0, h1*w1)
return conf_prob
@torch.no_grad()
def spvs_fine(data, config):
"""
Args:
data (dict): {
'b_ids': [M]
'i_ids': [M]
'j_ids': [M]
}
Update:
data (dict): {
conf_matrix_f_gt: [N, W_f^2, W_f^2], in original image resolution
}
"""
# 1. misc
device = data['image0'].device
N, _, H0, W0 = data['image0'].shape
_, _, H1, W1 = data['image1'].shape
scale = config['XOFTR']['RESOLUTION'][1]
scale0 = scale * data['scale0'][:, None] if 'scale0' in data else scale
scale1 = scale * data['scale1'][:, None] if 'scale1' in data else scale
h0, w0, h1, w1 = map(lambda x: x // scale, [H0, W0, H1, W1])
scale_f_c = config['XOFTR']['RESOLUTION'][0] // config['XOFTR']['RESOLUTION'][1]
W_f = config['XOFTR']['FINE_WINDOW_SIZE']
# 2. get coarse prediction
b_ids, i_ids, j_ids = data['b_ids'], data['i_ids'], data['j_ids']
if len(b_ids) == 0:
data.update({"conf_matrix_f_gt": torch.zeros(1,W_f*W_f,W_f*W_f, device=device)})
return
# 2. warp grids
# create kpts in meshgrid and resize them to image resolution
grid_pt0_c = create_meshgrid(h0, w0, False, device).repeat(N, 1, 1, 1)#.reshape(1, h0*w0, 2).repeat(N, 1, 1) # [N, hw, 2]
grid_pt0_i = scale0[:,None,...] * grid_pt0_c
grid_pt1_c = create_meshgrid(h1, w1, False, device).repeat(N, 1, 1, 1)#.reshape(1, h1*w1, 2).repeat(N, 1, 1)
grid_pt1_i = scale1[:,None,...] * grid_pt1_c
# unfold (crop windows) all local windows
stride_f = data['hw0_f'][0] // data['hw0_c'][0]
grid_pt0_i = rearrange(grid_pt0_i, 'n h w c -> n c h w')
grid_pt0_i = F.unfold(grid_pt0_i, kernel_size=(W_f, W_f), stride=stride_f, padding=W_f//2)
grid_pt0_i = rearrange(grid_pt0_i, 'n (c ww) l -> n l ww c', ww=W_f**2)
grid_pt0_i = grid_pt0_i[b_ids, i_ids]
grid_pt1_i = rearrange(grid_pt1_i, 'n h w c -> n c h w')
grid_pt1_i = F.unfold(grid_pt1_i, kernel_size=(W_f, W_f), stride=stride_f, padding=W_f//2)
grid_pt1_i = rearrange(grid_pt1_i, 'n (c ww) l -> n l ww c', ww=W_f**2)
grid_pt1_i = grid_pt1_i[b_ids, j_ids]
# warp kpts bi-directionally and resize them to fine-level resolution
# (no depth consistency check
# (unhandled edge case: points with 0-depth will be warped to the left-up corner)
_, w_pt0_i = warp_kpts_fine(grid_pt0_i, data['depth0'], data['depth1'], data['T_0to1'], data['K0'], data['K1'], b_ids)
_, w_pt1_i = warp_kpts_fine(grid_pt1_i, data['depth1'], data['depth0'], data['T_1to0'], data['K1'], data['K0'], b_ids)
w_pt0_f = w_pt0_i / scale1[b_ids]
w_pt1_f = w_pt1_i / scale0[b_ids]
mkpts0_c_scaled_to_f = torch.stack(
[i_ids % data['hw0_c'][1], i_ids // data['hw0_c'][1]],
dim=1) * scale_f_c - W_f//2
mkpts1_c_scaled_to_f = torch.stack(
[j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]],
dim=1) * scale_f_c - W_f//2
w_pt0_f = w_pt0_f - mkpts1_c_scaled_to_f[:,None,:]
w_pt1_f = w_pt1_f - mkpts0_c_scaled_to_f[:,None,:]
# 3. check if mutual nearest neighbor
w_pt0_f_round = w_pt0_f[:, :, :].round().long()
w_pt1_f_round = w_pt1_f[:, :, :].round().long()
M = w_pt0_f.shape[0]
nearest_index1 = w_pt0_f_round[..., 0] + w_pt0_f_round[..., 1] * W_f
nearest_index0 = w_pt1_f_round[..., 0] + w_pt1_f_round[..., 1] * W_f
# corner case: out of boundary
def out_bound_mask(pt, w, h):
return (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h)
nearest_index1[out_bound_mask(w_pt0_f_round, W_f, W_f)] = 0
nearest_index0[out_bound_mask(w_pt1_f_round, W_f, W_f)] = 0
loop_back = torch.stack([nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0)
correct_0to1 = loop_back == torch.arange(W_f*W_f, device=device)[None].repeat(M, 1)
correct_0to1[:, 0] = False # ignore the top-left corner
# 4. construct a gt conf_matrix
conf_matrix_f_gt = torch.zeros(M, W_f*W_f, W_f*W_f, device=device)
b_ids, i_ids = torch.where(correct_0to1 != 0)
j_ids = nearest_index1[b_ids, i_ids]
conf_matrix_f_gt[b_ids, i_ids, j_ids] = 1
data.update({"conf_matrix_f_gt": conf_matrix_f_gt})
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