File size: 8,454 Bytes
0a82b18 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 |
from pathlib import Path
import yaml
import py3_wget
import cv2
import numpy as np
import os
import shutil
import torchvision.transforms as tfm
import gdown
from matching.utils import add_to_path, resize_to_divisible
from matching import WEIGHTS_DIR, THIRD_PARTY_DIR, BaseMatcher
BASE_PATH = THIRD_PARTY_DIR.joinpath("imatch-toolbox")
add_to_path(BASE_PATH)
import immatch
class Patch2pixMatcher(BaseMatcher):
# url1 = 'https://vision.in.tum.de/webshare/u/zhouq/patch2pix/pretrained/patch2pix_pretrained.pth'
pretrained_src = "https://drive.google.com/file/d/1SyIAKza_PMlYsj6D72yOQjg2ZASXTjBd/view"
url2 = "https://vision.in.tum.de/webshare/u/zhouq/patch2pix/pretrained/ncn_ivd_5ep.pth"
model_path = WEIGHTS_DIR.joinpath("patch2pix_pretrained.pth")
divisible_by = 32
def __init__(self, device="cpu", *args, **kwargs):
super().__init__(device, **kwargs)
with open(BASE_PATH.joinpath("configs/patch2pix.yml"), "r") as f:
args = yaml.load(f, Loader=yaml.FullLoader)["sat"]
if not Patch2pixMatcher.model_path.is_file():
self.download_weights()
args["ckpt"] = str(Patch2pixMatcher.model_path)
print(args)
self.matcher = immatch.__dict__[args["class"]](args)
self.matcher.model.to(device)
self.normalize = tfm.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
@staticmethod
def download_weights():
print("Downloading Patch2Pix model weights...")
WEIGHTS_DIR.mkdir(exist_ok=True)
gdown.download(
Patch2pixMatcher.pretrained_src,
output=str(Patch2pixMatcher.model_path),
fuzzy=True,
)
# urllib.request.urlretrieve(Patch2pixMatcher.pretrained_src, ckpt)
# urllib.request.urlretrieve(Patch2pixMatcher.url2, ncn_ckpt)
def preprocess(self, img):
img = resize_to_divisible(img, self.divisible_by)
return self.normalize(img).unsqueeze(0)
def _forward(self, img0, img1):
img0 = self.preprocess(img0)
img1 = self.preprocess(img1)
# Fine matches
fine_matches, fine_scores, coarse_matches = self.matcher.model.predict_fine(
img0, img1, ksize=self.matcher.ksize, ncn_thres=0.0, mutual=True
)
coarse_matches = coarse_matches[0].cpu().data.numpy()
fine_matches = fine_matches[0].cpu().data.numpy()
fine_scores = fine_scores[0].cpu().data.numpy()
# Inlier filtering
pos_ids = np.where(fine_scores > self.matcher.match_threshold)[0]
if len(pos_ids) > 0:
coarse_matches = coarse_matches[pos_ids]
matches = fine_matches[pos_ids]
# scores = fine_scores[pos_ids]
else:
# Simply take all matches for this case
matches = fine_matches
# scores = fine_scores
mkpts0 = matches[:, :2]
mkpts1 = matches[:, 2:4]
return mkpts0, mkpts1, None, None, None, None
class SuperGlueMatcher(BaseMatcher):
def __init__(self, device="cpu", max_num_keypoints=2048, *args, **kwargs):
super().__init__(device, **kwargs)
self.to_gray = tfm.Grayscale()
with open(BASE_PATH.joinpath("configs/superglue.yml"), "r") as f:
args = yaml.load(f, Loader=yaml.FullLoader)["sat"]
args["max_keypoints"] = max_num_keypoints
self.matcher = immatch.__dict__[args["class"]](args)
# move models to proper device - immatch reads cuda available and defaults to GPU
self.matcher.model.to(device) # SG
self.matcher.detector.model.to(device) # SP
self.match_threshold = args["match_threshold"]
# print(self.matcher.detector.model.config)
def _forward(self, img0, img1):
img0_gray = self.to_gray(img0).unsqueeze(0).to(self.device)
img1_gray = self.to_gray(img1).unsqueeze(0).to(self.device)
matches, kpts0, kpts1, _ = self.matcher.match_inputs_(img0_gray, img1_gray)
mkpts0 = matches[:, :2]
mkpts1 = matches[:, 2:4]
return mkpts0, mkpts1, kpts0, kpts1, None, None
class R2D2Matcher(BaseMatcher):
def __init__(self, device="cpu", max_num_keypoints=2048, *args, **kwargs):
super().__init__(device, **kwargs)
self.normalize = tfm.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
with open(BASE_PATH.joinpath("configs/r2d2.yml"), "r") as f:
args = yaml.load(f, Loader=yaml.FullLoader)["sat"]
args["ckpt"] = BASE_PATH.joinpath(args["ckpt"])
args["top_k"] = max_num_keypoints
self.get_model_weights(args["ckpt"])
self.model = immatch.__dict__[args["class"]](args)
# move models to proper device - immatch reads cuda available and defaults to GPU
self.model.model.to(device)
self.match_threshold = args["match_threshold"]
@staticmethod
def get_model_weights(model_path):
if not os.path.isfile(model_path):
print("Getting R2D2 model weights...")
shutil.copytree(
Path(f"{BASE_PATH}/third_party/r2d2/models"),
Path(f"{BASE_PATH}/pretrained/r2d2"),
)
def _forward(self, img0, img1):
img0 = self.normalize(img0).unsqueeze(0).to(self.device)
img1 = self.normalize(img1).unsqueeze(0).to(self.device)
kpts0, desc0 = self.model.extract_features(img0)
kpts1, desc1 = self.model.extract_features(img1)
# NN Match
match_ids, scores = self.model.mutual_nn_match(desc0, desc1, threshold=self.match_threshold)
mkpts0 = kpts0[match_ids[:, 0], :2].cpu().numpy()
mkpts1 = kpts1[match_ids[:, 1], :2].cpu().numpy()
return mkpts0, mkpts1, kpts0, kpts1, desc0, desc1
class D2netMatcher(BaseMatcher):
def __init__(self, device="cpu", *args, **kwargs):
super().__init__(device, **kwargs)
with open(BASE_PATH.joinpath("configs/d2net.yml"), "r") as f:
args = yaml.load(f, Loader=yaml.FullLoader)["sat"]
args["ckpt"] = BASE_PATH.joinpath(args["ckpt"])
if not os.path.isfile(args["ckpt"]):
print("Downloading D2Net model weights...")
os.makedirs(os.path.dirname(args["ckpt"]), exist_ok=True)
py3_wget.download_file("https://dusmanu.com/files/d2-net/d2_tf.pth", args["ckpt"])
self.model = immatch.__dict__[args["class"]](args)
self.match_threshold = args["match_threshold"]
@staticmethod
def preprocess(img_tensor):
image = img_tensor.cpu().numpy().astype(np.float32)
# convert to 0-255
image = (image * 255).astype(int).astype(np.float32)
# RGB -> BGR
image = image[::-1, :, :]
# Zero-center by mean pixel
mean = np.array([103.939, 116.779, 123.68])
image = image - mean.reshape([3, 1, 1])
return image
def _forward(self, img0, img1):
img0 = self.preprocess(img0)
img1 = self.preprocess(img1)
kpts0, desc0 = self.model.extract_features(img0)
kpts1, desc1 = self.model.extract_features(img1)
match_ids, _ = self.model.mutual_nn_match(desc0, desc1, threshold=self.match_threshold)
mkpts0 = kpts0[match_ids[:, 0], :2]
mkpts1 = kpts1[match_ids[:, 1], :2]
return mkpts0, mkpts1, kpts0, kpts1, desc0, desc1
class DogAffHardNNMatcher(BaseMatcher):
def __init__(self, device="cpu", max_num_keypoints=2048, *args, **kwargs):
super().__init__(device, **kwargs)
with open(BASE_PATH.joinpath("configs/dogaffnethardnet.yml"), "r") as f:
args = yaml.load(f, Loader=yaml.FullLoader)["example"]
args["npts"] = max_num_keypoints
self.model = immatch.__dict__[args["class"]](args)
self.to_gray = tfm.Grayscale()
@staticmethod
def tensor_to_numpy_int(im_tensor):
im_arr = im_tensor.cpu().numpy().transpose(1, 2, 0)
im = cv2.cvtColor(im_arr, cv2.COLOR_RGB2GRAY)
im = cv2.normalize(im, None, 0, 255, cv2.NORM_MINMAX).astype("uint8")
return im
def _forward(self, img0, img1):
# convert tensors to numpy 255-based for OpenCV
img0 = self.tensor_to_numpy_int(img0)
img1 = self.tensor_to_numpy_int(img1)
matches, _, _, _ = self.model.match_inputs_(img0, img1)
mkpts0 = matches[:, :2]
mkpts1 = matches[:, 2:4]
return mkpts0, mkpts1, None, None, None, None
|