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import torch
from pathlib import Path
import py3_wget
import torchvision.transforms as tfm
from argparse import Namespace
import kornia
from matching import WEIGHTS_DIR, THIRD_PARTY_DIR, BaseMatcher
from matching.utils import to_numpy, resize_to_divisible, add_to_path
add_to_path(THIRD_PARTY_DIR.joinpath("MINIMA"), insert=0)
add_to_path(THIRD_PARTY_DIR.joinpath("MINIMA/third_party/RoMa"))
from src.utils.load_model import load_model, load_sp_lg, load_loftr, load_roma
class MINIMAMatcher(BaseMatcher):
weights_minima_sp_lg = (
"https://github.com/LSXI7/storage/releases/download/MINIMA/minima_lightglue.pth"
)
weights_minima_roma = (
"https://github.com/LSXI7/storage/releases/download/MINIMA/minima_roma.pth"
)
weights_minima_loftr = (
"https://github.com/LSXI7/storage/releases/download/MINIMA/minima_loftr.ckpt"
)
model_path_sp_lg = WEIGHTS_DIR.joinpath("minima_lightglue.ckpt")
model_path_roma = WEIGHTS_DIR.joinpath("minima_roma.ckpt")
model_path_loftr = WEIGHTS_DIR.joinpath("minima_loftr.ckpt")
ALLOWED_TYPES = ["roma", "sp_lg", "loftr"]
def __init__(self, device="cpu", model_type="sp_lg", **kwargs):
super().__init__(device, **kwargs)
self.model_type = model_type.lower()
self.model_args = Namespace()
assert (
self.model_type in MINIMAMatcher.ALLOWED_TYPES
), f"model type must be in {MINIMAMatcher.ALLOWED_TYPES}, you passed {self.model_type}"
self.download_weights()
def download_weights(self):
if not Path(self.weights_src).is_file():
print(f"Downloading MINIMA {self.model_type}...")
py3_wget.download_file(self.weights_src, self.model_path)
class MINIMASpLgMatcher(MINIMAMatcher):
weights_src = (
"https://github.com/LSXI7/storage/releases/download/MINIMA/minima_lightglue.pth"
)
model_path = WEIGHTS_DIR.joinpath("minima_lightglue.ckpt")
def __init__(self, device="cpu", **kwargs):
super().__init__(device, **kwargs)
self.model_args.ckpt = self.model_path_sp_lg
self.matcher = load_sp_lg(self.model_args).model.to(self.device)
def preprocess(self, img):
_, h, w = img.shape
orig_shape = h, w
return img.unsqueeze(0).to(self.device), orig_shape
def _forward(self, img0, img1):
img0, img0_orig_shape = self.preprocess(img0)
img1, img1_orig_shape = self.preprocess(img1)
# print(img0.shape, img1.shape)
batch = {"image0": img0, "image1": img1}
batch = self.matcher(batch)
mkpts0 = to_numpy(batch["keypoints0"])
mkpts1 = to_numpy(batch["keypoints1"])
H0, W0, H1, W1 = *img0.shape[-2:], *img1.shape[-2:]
mkpts0 = self.rescale_coords(mkpts0, *img0_orig_shape, H0, W0)
mkpts1 = self.rescale_coords(mkpts1, *img1_orig_shape, H1, W1)
return mkpts0, mkpts1, None, None, None, None
class MINIMALoFTRMatcher(MINIMAMatcher):
weights_src = (
"https://github.com/LSXI7/storage/releases/download/MINIMA/minima_loftr.ckpt"
)
model_path = WEIGHTS_DIR.joinpath("minima_loftr.ckpt")
def __init__(self, device="cpu", **kwargs):
super().__init__(device, **kwargs)
self.model_args.thr = 0.2
self.model_args.ckpt = self.model_path_loftr
self.matcher = load_loftr(self.model_args).model.to(self.device)
def preprocess(self, img):
_, h, w = img.shape
orig_shape = h, w
img = tfm.Grayscale()(img)
return img.unsqueeze(0).to(self.device), orig_shape
def _forward(self, img0, img1):
img0, img0_orig_shape = self.preprocess(img0)
img1, img1_orig_shape = self.preprocess(img1)
batch = {"image0": img0, "image1": img1}
self.matcher(batch)
mkpts0 = to_numpy(batch["mkpts0_f"])
mkpts1 = to_numpy(batch["mkpts1_f"])
H0, W0, H1, W1 = *img0.shape[-2:], *img1.shape[-2:]
mkpts0 = self.rescale_coords(mkpts0, *img0_orig_shape, H0, W0)
mkpts1 = self.rescale_coords(mkpts1, *img1_orig_shape, H1, W1)
return mkpts0, mkpts1, None, None, None, None
class MINIMARomaMatcher(MINIMAMatcher):
weights_src = (
"https://github.com/LSXI7/storage/releases/download/MINIMA/minima_roma.pth"
)
model_path = WEIGHTS_DIR.joinpath("minima_roma.ckpt")
ALLOWABLE_MODEL_SIZES = ["tiny", "large"]
def __init__(self, device="cpu", model_size="tiny", **kwargs):
super().__init__(device, **kwargs)
assert model_size in self.ALLOWABLE_MODEL_SIZES
self.model_args.ckpt = self.model_path_roma
self.model_args.ckpt2 = model_size
self.matcher = load_roma(self.model_args).model.eval().to(self.device)
def preprocess(self, img):
_, h, w = img.shape
orig_shape = h, w
return tfm.ToPILImage()(img.to(self.device)), orig_shape
def _forward(self, img0, img1):
img0, img0_orig_shape = self.preprocess(img0)
img1, img1_orig_shape = self.preprocess(img1)
orig_H0, orig_W0 = img0_orig_shape
orig_H1, orig_W1 = img1_orig_shape
warp, certainty = self.matcher.match(img0, img1, batched=False)
matches, mconf = self.matcher.sample(warp, certainty)
mkpts0, mkpts1 = self.matcher.to_pixel_coordinates(
matches, orig_H0, orig_W0, orig_H1, orig_W1
)
(W0, H0), (W1, H1) = img0.size, img1.size
mkpts0 = self.rescale_coords(mkpts0, *img0_orig_shape, H0, W0)
mkpts1 = self.rescale_coords(mkpts1, *img1_orig_shape, H1, W1)
return mkpts0, mkpts1, None, None, None, None
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