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Running
on
Zero
File size: 6,233 Bytes
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import os
import cv2
import torch
import warnings
import numpy as np
from os.path import join
from pathlib import Path
from tools import get_padding_size
from hloc.utils import CLS_DICT, exclude
from ..utils.base_model import BaseModel
from networks.dkm.models.model_zoo.DKMv3 import DKMv3
class LoFTR(BaseModel):
default_conf = {
'max_num_matches': None,
}
required_inputs = [
'image0',
'image1'
]
def _init(self, conf):
self.h = 672
self.w = 896
model = DKMv3(None, self.h, self.w, upsample_preds=True)
checkpoints_path = join('weights', conf['weights'])
state_dict = torch.load(checkpoints_path, map_location='cpu')
if 'state_dict' in state_dict.keys(): state_dict = state_dict['state_dict']
for k in list(state_dict.keys()):
if k.startswith('model.'):
state_dict[k.replace('model.', '', 1)] = state_dict.pop(k)
if 'encoder.net.fc' in k:
state_dict.pop(k)
model.load_state_dict(state_dict)
self.net = model
def _forward(self, data):
outputs = Path(os.environ['GIMRECONSTRUCTION'])
segment_root = outputs / '..' / 'segment'
# For consistency with hloc pairs, we refine kpts in image0!
rename = {
'keypoints0': 'keypoints1',
'keypoints1': 'keypoints0',
'image0': 'image1',
'image1': 'image0',
'mask0': 'mask1',
'mask1': 'mask0',
'name0': 'name1',
'name1': 'name0',
}
data_ = {rename[k]: v for k, v in data.items()}
with warnings.catch_warnings():
warnings.simplefilter("ignore")
image0, image1 = data_['image0'], data_['image1']
img0, img1 = data_['name0'], data_['name1']
# segment image
seg_path0 = join(segment_root, '{}.npy'.format(img0[:-4]))
mask0 = np.load(seg_path0)
if mask0.shape[:2] != image0.shape[-2:]:
mask0 = cv2.resize(mask0, image0.shape[-2:][::-1],
interpolation=cv2.INTER_NEAREST)
mask_0 = mask0 != CLS_DICT[exclude[0]]
for cls in exclude[1:]:
mask_0 = mask_0 & (mask0 != CLS_DICT[cls])
mask_0 = mask0
mask_0 = mask_0.astype(np.uint8)
mask_0 = torch.from_numpy((mask_0 == 0).astype(np.uint8)).to(image0.device)
mask_0 = mask_0.float()[None, None] == 0
image0 = image0 * mask_0
# segment image
seg_path1 = join(segment_root, '{}.npy'.format(img1[:-4]))
mask1 = np.load(seg_path1)
if mask1.shape != image1.shape[-2:]:
mask1 = cv2.resize(mask1, image1.shape[-2:][::-1],
interpolation=cv2.INTER_NEAREST)
mask_1 = mask1 != CLS_DICT[exclude[0]]
for cls in exclude[1:]:
mask_1 = mask_1 & (mask1 != CLS_DICT[cls])
mask_1 = mask1
mask_1 = mask_1.astype(np.uint8)
mask_1 = torch.from_numpy((mask_1 == 0).astype(np.uint8)).to(image1.device)
mask_1 = mask_1.float()[None, None] == 0
image1 = image1 * mask_1
orig_width0, orig_height0, pad_left0, pad_right0, pad_top0, pad_bottom0 = get_padding_size(image0, self.h, self.w)
orig_width1, orig_height1, pad_left1, pad_right1, pad_top1, pad_bottom1 = get_padding_size(image1, self.h, self.w)
image0 = torch.nn.functional.pad(image0, (pad_left0, pad_right0, pad_top0, pad_bottom0))
image1 = torch.nn.functional.pad(image1, (pad_left1, pad_right1, pad_top1, pad_bottom1))
dense_matches, dense_certainty = self.net.match(image0, image1)
sparse_matches, mconf = self.net.sample(dense_matches, dense_certainty, 8192)
m = mconf > 0
mconf = mconf[m]
sparse_matches = sparse_matches[m]
height0, width0 = image0.shape[-2:]
height1, width1 = image1.shape[-2:]
kpts0 = sparse_matches[:, :2]
kpts0 = torch.stack((width0 * (kpts0[:, 0] + 1) / 2,
height0 * (kpts0[:, 1] + 1) / 2), dim=-1, )
kpts1 = sparse_matches[:, 2:]
kpts1 = torch.stack((width1 * (kpts1[:, 0] + 1) / 2,
height1 * (kpts1[:, 1] + 1) / 2), dim=-1, )
b_ids, i_ids = torch.where(mconf[None])
# before padding
kpts0 -= kpts0.new_tensor((pad_left0, pad_top0))[None]
kpts1 -= kpts1.new_tensor((pad_left1, pad_top1))[None]
mask = (kpts0[:, 0] > 0) & \
(kpts0[:, 1] > 0) & \
(kpts1[:, 0] > 0) & \
(kpts1[:, 1] > 0)
mask = mask & \
(kpts0[:, 0] <= (orig_width0 - 1)) & \
(kpts1[:, 0] <= (orig_width1 - 1)) & \
(kpts0[:, 1] <= (orig_height0 - 1)) & \
(kpts1[:, 1] <= (orig_height1 - 1))
pred = {
'keypoints0': kpts0[i_ids],
'keypoints1': kpts1[i_ids],
'confidence': mconf[i_ids],
'batch_indexes': b_ids,
}
# noinspection PyUnresolvedReferences
scores, b_ids = pred['confidence'], pred['batch_indexes']
kpts0, kpts1 = pred['keypoints0'], pred['keypoints1']
pred['confidence'], pred['batch_indexes'] = scores[mask], b_ids[mask]
pred['keypoints0'], pred['keypoints1'] = kpts0[mask], kpts1[mask]
scores = pred['confidence']
top_k = self.conf['max_num_matches']
if top_k is not None and len(scores) > top_k:
keep = torch.argsort(scores, descending=True)[:top_k]
pred['keypoints0'], pred['keypoints1'] =\
pred['keypoints0'][keep], pred['keypoints1'][keep]
scores = scores[keep]
# Switch back indices
pred = {(rename[k] if k in rename else k): v for k, v in pred.items()}
pred['scores'] = scores
del pred['confidence']
return pred
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