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import os
import sys
import torch
import numpy as np
import math
import cv2
from models.model import LiftFeatSPModel
from models.interpolator import InterpolateSparse2d
from utils.config import featureboost_config
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
MODEL_PATH = os.path.join(os.path.dirname(__file__), "../weights/LiftFeat.pth")
class NonMaxSuppression(torch.nn.Module):
def __init__(self, rep_thr=0.1, top_k=4096):
super(NonMaxSuppression, self).__init__()
self.max_filter = torch.nn.MaxPool2d(kernel_size=5, stride=1, padding=2)
self.rep_thr = rep_thr
self.top_k = top_k
def NMS(self, x, threshold=0.05, kernel_size=5):
B, _, H, W = x.shape
pad = kernel_size // 2
local_max = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=pad)(x)
pos = (x == local_max) & (x > threshold)
pos_batched = [k.nonzero()[..., 1:].flip(-1) for k in pos]
pad_val = max([len(x) for x in pos_batched])
pos = torch.zeros((B, pad_val, 2), dtype=torch.long, device=x.device)
# Pad kpts and build (B, N, 2) tensor
for b in range(len(pos_batched)):
pos[b, : len(pos_batched[b]), :] = pos_batched[b]
return pos
def forward(self, score):
pos = self.NMS(score, self.rep_thr)
return pos
def load_model(model, weight_path):
pretrained_weights = torch.load(weight_path, map_location="cpu")
model_keys = set(model.state_dict().keys())
pretrained_keys = set(pretrained_weights.keys())
missing_keys = model_keys - pretrained_keys
unexpected_keys = pretrained_keys - model_keys
# if missing_keys:
# print("Missing keys in pretrained weights:", missing_keys)
# else:
# print("No missing keys in pretrained weights.")
# if unexpected_keys:
# print("Unexpected keys in pretrained weights:", unexpected_keys)
# else:
# print("No unexpected keys in pretrained weights.")
if not missing_keys and not unexpected_keys:
model.load_state_dict(pretrained_weights)
print("load weight successfully.")
else:
model.load_state_dict(pretrained_weights, strict=False)
# print("There were issues with the keys.")
return model
import torch.nn as nn
class LiftFeat(nn.Module):
def __init__(self, weight=MODEL_PATH, top_k=4096, detect_threshold=0.1):
super().__init__()
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.net = LiftFeatSPModel(featureboost_config).to(self.device).eval()
self.top_k = top_k
self.sampler = InterpolateSparse2d("bicubic")
self.net = load_model(self.net, weight)
self.detector = NonMaxSuppression(rep_thr=detect_threshold)
self.net = self.net.to(self.device)
self.detector = self.detector.to(self.device)
self.sampler = self.sampler.to(self.device)
def image_preprocess(self, image: np.ndarray):
H, W, C = image.shape[0], image.shape[1], image.shape[2]
_H = math.ceil(H / 32) * 32
_W = math.ceil(W / 32) * 32
pad_h = _H - H
pad_w = _W - W
image = cv2.copyMakeBorder(image, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, None, (0, 0, 0))
pad_info = [0, pad_h, 0, pad_w]
if len(image.shape) == 3:
image = image[None, ...]
image = torch.tensor(image).permute(0, 3, 1, 2) / 255
image = image.to(device)
return image, pad_info
@torch.inference_mode()
def extract(self, image: np.ndarray):
image, pad_info = self.image_preprocess(image)
B, _, _H1, _W1 = image.shape
M1, K1, D1 = self.net.forward1(image)
refine_M = self.net.forward2(M1, K1, D1)
refine_M = refine_M.reshape(M1.shape[0], M1.shape[2], M1.shape[3], -1).permute(0, 3, 1, 2)
refine_M = torch.nn.functional.normalize(refine_M, 2, dim=1)
descs_map = refine_M
scores = torch.softmax(K1, dim=1)[:, :64]
heatmap = scores.permute(0, 2, 3, 1).reshape(scores.shape[0], scores.shape[2], scores.shape[3], 8, 8)
heatmap = heatmap.permute(0, 1, 3, 2, 4).reshape(scores.shape[0], 1, scores.shape[2] * 8, scores.shape[3] * 8)
pos = self.detector(heatmap)
kpts = pos.squeeze(0)
mask_w = kpts[..., 0] < (_W1 - pad_info[-1])
kpts = kpts[mask_w]
mask_h = kpts[..., 1] < (_H1 - pad_info[1])
kpts = kpts[mask_h]
scores = self.sampler(heatmap, kpts.unsqueeze(0), _H1, _W1)
scores = scores.squeeze(0).reshape(-1)
descs = self.sampler(descs_map, kpts.unsqueeze(0), _H1, _W1)
descs = torch.nn.functional.normalize(descs, p=2, dim=1)
descs = descs.squeeze(0)
return {"descriptors": descs, "keypoints": kpts, "scores": scores}
def match_liftfeat(self, img1, img2, min_cossim=-1):
# import pdb;pdb.set_trace()
data1 = self.extract(img1)
data2 = self.extract(img2)
kpts1, feats1 = data1["keypoints"], data1["descriptors"]
kpts2, feats2 = data2["keypoints"], data2["descriptors"]
cossim = feats1 @ feats2.t()
cossim_t = feats2 @ feats1.t()
_, match12 = cossim.max(dim=1)
_, match21 = cossim_t.max(dim=1)
idx0 = torch.arange(len(match12), device=match12.device)
mutual = match21[match12] == idx0
if min_cossim > 0:
cossim, _ = cossim.max(dim=1)
good = cossim > min_cossim
idx0 = idx0[mutual & good]
idx1 = match12[mutual & good]
else:
idx0 = idx0[mutual]
idx1 = match12[mutual]
mkpts1, mkpts2 = kpts1[idx0], kpts2[idx1]
mkpts1, mkpts2 = mkpts1.cpu().numpy(), mkpts2.cpu().numpy()
return mkpts1, mkpts2
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