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import numpy as np
import cv2
from lightglue import LightGlue
from lightglue.utils import rbd
def unrotate_kps_W(kps_rot, k, H, W):
import numpy as np
# Ensure inputs are Numpy
if hasattr(kps_rot, 'cpu'): kps_rot = kps_rot.cpu().numpy()
if hasattr(k, 'cpu'): k = k.cpu().numpy()
# Squeeze if necessary
if k.ndim > 1: k = k.squeeze()
if kps_rot.ndim > 2: kps_rot = kps_rot.squeeze()
x_r = kps_rot[:, 0]
y_r = kps_rot[:, 1]
x = np.zeros_like(x_r)
y = np.zeros_like(y_r)
mask0 = (k == 0)
x[mask0], y[mask0] = x_r[mask0], y_r[mask0]
mask1 = (k == 1)
x[mask1], y[mask1] = (W - 1) - y_r[mask1], x_r[mask1]
mask2 = (k == 2)
x[mask2], y[mask2] = (W - 1) - x_r[mask2], (H - 1) - y_r[mask2]
mask3 = (k == 3)
x[mask3], y[mask3] = y_r[mask3], (H - 1) - x_r[mask3]
return np.stack([x, y], axis=-1)
def unrotate_kps(kps_rot, k, H, W):
import torch
# k is how many times you rotated CCW by 90° to create the rotated image
x_r, y_r = kps_rot[:, 0].clone(), kps_rot[:, 1].clone()
if k == 0:
x, y = x_r, y_r
elif k == 1: # 90° CCW
x = (W - 1) - y_r
y = x_r
elif k == 2: # 180°
x = (W - 1) - x_r
y = (H - 1) - y_r
elif k == 3: # 270° CCW
x = y_r
y = (H - 1) - x_r
else:
raise ValueError("k must be 0..3")
return torch.stack([x, y], dim=-1)
# def lightglue_matching(path_to_image0, path_to_image1, plot=False, features='superpoint'):
# from lightglue import LightGlue, SuperPoint, SIFT
# from lightglue.utils import load_image, rbd
# from lightglue import viz2d
# import torch
# # --- Models on GPU ---
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
# if features == 'superpoint':
# extractor = SuperPoint(max_num_keypoints=2048).eval().to(device)
# if features == 'sift':
# extractor = SIFT(max_num_keypoints=2048).eval().to(device)
# matcher = LightGlue(features=features).eval().to(device)
# # --- Load images as Torch tensors (3,H,W) in [0,1] ---
# timg0 = load_image(path_to_image0).to(device)
# timg1 = load_image(path_to_image1).to(device)
# # --- Extract local features ---
# feats0 = extractor.extract(timg0) # auto-resize inside
# max_num_matches = -1
# best_k = 0
# best_feats0 = None
# best_feats1 = None
# for k in range(4):
# timg1_rotated = torch.rot90(timg1, k, dims=(1, 2))
# feats1_k = extractor.extract(timg1_rotated)
# out_k = matcher({'image0': feats0, 'image1': feats1_k})
# feats0_k, feats1_k, out_k = [rbd(x) for x in [feats0, feats1_k, out_k]] # remove batch dim
# matches_k = out_k['matches'] # (K,2) long
# num_k = len(matches_k)
# if num_k > max_num_matches:
# max_num_matches = num_k
# matches = matches_k
# best_feats0 = feats0_k
# best_feats1 = feats1_k
# best_k = k
# # --- Keypoints in matched order (Torch tensors on CPU) ---
# H1, W1 = timg1.shape[-2], timg1.shape[-1]
# kpts0 = best_feats0['keypoints'][matches[:, 0]]
# kpts1 = best_feats1['keypoints'][matches[:, 1]]
# kpts1 = unrotate_kps(kpts1, best_k, H1, W1) # (K,2) mapped to original image1 coords
# desc0 = best_feats0['descriptors'][matches[:, 0]]
# desc1 = best_feats1['descriptors'][matches[:, 1]]
# if plot:
# if len(kpts0) == 0 or len(kpts1) == 0:
# print("No matches found.")
# return None, None
# ax = viz2d.plot_images([timg0.cpu(), timg1.cpu()])
# viz2d.plot_matches(kpts0.cpu(), kpts1.cpu(), color=None, lw=0.8, axes=ax)
# #ax0 = ax[0] if isinstance(ax, (list, tuple, np.ndarray)) else ax
# #fig = ax0.figure
# #return kpts0, kpts1 #, fig, ax
# return kpts0, kpts1, desc0, desc1
def lightglue_keypoints(path_to_image0, features='superpoint', rotations = [0,1,2,3]):
from lightglue import LightGlue, SuperPoint, SIFT
from lightglue.utils import load_image, rbd
from lightglue import viz2d
import torch
# --- Models on GPU ---
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if features == 'superpoint':
extractor = SuperPoint(max_num_keypoints=2048).eval().to(device)
if features == 'sift':
extractor = SIFT(max_num_keypoints=2048).eval().to(device)
# --- Load images as Torch tensors (3,H,W) in [0,1] ---
timg = load_image(path_to_image0).to(device)
_, h, w = timg.shape
# --- Extract local features ---
feats = {}
for k in (rotations):
timg_rotated = torch.rot90(timg, k, dims=(1, 2))
feats[k] = extractor.extract(timg_rotated)
print(f"Extracted {feats[k]['keypoints'].shape[1]} keypoints for rotation {k}")
# --- Merge features back to original coordinate system ---
all_keypoints = []
all_scores = []
all_descriptors = []
all_rotations = []
for k, feat in feats.items():
kpts = feat['keypoints'] # Shape (1, N, 2)
num_kpts = kpts.shape[1]
if k == 0:
kpts_corrected = kpts
elif k == 1:
kpts_corrected = torch.stack(
[w - 1 - kpts[..., 1], kpts[..., 0]], dim=-1
)
elif k == 2:
kpts_corrected = torch.stack(
[w - 1 - kpts[..., 0], h - 1 - kpts[..., 1]], dim=-1
)
elif k == 3:
kpts_corrected = torch.stack(
[kpts[..., 1], h - 1 - kpts[..., 0]], dim=-1
)
rot_indices = torch.full((1, num_kpts), k, dtype=torch.long, device=device)
all_keypoints.append(feat['keypoints'])
all_scores.append(feat['keypoint_scores'])
all_descriptors.append(feat['descriptors'])
all_rotations.append(rot_indices)
# Concatenate all features along the keypoint dimension (dim=1)
feats_merged = {
'keypoints': torch.cat(all_keypoints, dim=1),
'keypoint_scores': torch.cat(all_scores, dim=1),
'descriptors': torch.cat(all_descriptors, dim=1),
'rotations': torch.cat(all_rotations, dim=1)
}
num_kpts = feats_merged['keypoints'].shape[1]
# perm = torch.randperm(num_kpts, device=device)
# feats_merged['keypoints'] = feats_merged['keypoints'][:, perm, :]
# feats_merged['keypoint_scores'] = feats_merged['keypoint_scores'][:, perm]
# feats_merged['descriptors'] = feats_merged['descriptors'][:, perm, :]
# Optional: If you want to retain other keys like 'shape' or 'image_size'
feats_merged['image_size'] = torch.tensor([w, h], device=device).unsqueeze(0)
return feats_merged , h, w
def lightglue_matching(feats0, feats1, plot=False, features='superpoint', path_to_image0=None, path_to_image1=None):
from lightglue import LightGlue, SuperPoint, SIFT
from lightglue.utils import load_image, rbd
from lightglue import viz2d
import torch
# --- Models on GPU ---
device = 'cuda' if torch.cuda.is_available() else 'cpu'
matcher = LightGlue(features=features).eval().to(device)
# --- Load images as Torch tensors (3,H,W) in [0,1] ---
if plot:
timg0 = load_image(path_to_image0).to(device)
timg1 = load_image(path_to_image1).to(device)
# --- Extract local features ---
max_num_matches = -1
best_k = 0
best_feats0 = None
best_feats1 = None
for k in range(1):
#timg1_rotated = torch.rot90(timg1, k, dims=(1, 2))
feats1_k = feats1 #extractor.extract(timg1_rotated)
out_k = matcher({'image0': feats0, 'image1': feats1_k})
feats0_k, feats1_k, out_k = [rbd(x) for x in [feats0, feats1_k, out_k]] # remove batch dim
matches_k = out_k['matches'] # (K,2) long
num_k = len(matches_k)
if num_k > max_num_matches:
max_num_matches = num_k
matches = matches_k
best_feats0 = feats0_k
best_feats1 = feats1_k
best_k = k
print(f"LightGlue found {len(matches)} matches.")
# --- Keypoints in matched order (Torch tensors on CPU) ---
#H1, W1 = timg1.shape[-2], timg1.shape[-1]
# kpts0 = best_feats0['keypoints'][matches[:, 0]]
# kpts1 = best_feats1['keypoints'][matches[:, 1]]
# #kpts1 = unrotate_kps(kpts1, best_k, H1, W1) # (K,2) mapped to original image1 coords
# desc0 = best_feats0['descriptors'][matches[:, 0]]
# desc1 = best_feats1['descriptors'][matches[:, 1]]
# pts0 = kpts0.detach().cpu().numpy().astype(np.float32) # (K,2)
# pts1 = kpts1.detach().cpu().numpy().astype(np.float32) # (K,2)
# H, inliers = cv2.findHomography(pts0, pts1, cv2.RANSAC, 5.0)
# if inliers is not None:
# mask = inliers.ravel() == 1
# mask_tensor = torch.from_numpy(mask).to(matches.device)
# matches = matches[mask_tensor]
# else:
# # If geometry check failed completely, return no matches
# return None
# if plot:
# if len(kpts0) == 0 or len(kpts1) == 0:
# print("No matches found.")
# return None, None
# ax = viz2d.plot_images([timg0.cpu(), timg1.cpu()])
# viz2d.plot_matches(kpts0.cpu(), kpts1.cpu(), color=None, lw=0.8, axes=ax)
# #ax0 = ax[0] if isinstance(ax, (list, tuple, np.ndarray)) else ax
# #fig = ax0.figure
# #return kpts0, kpts1 #, fig, ax
return matches |