LoFTR算法Python代码实现
时间: 2024-06-08 11:03:37 浏览: 14
LoFTR(Local Feature based TRansformer)是一种新的图像匹配算法,它结合了局部特征和Transformer模型,实现了端到端的图像匹配。以下是LoFTR算法Python代码实现的步骤:
1. 下载LoFTR源码,安装依赖项:numpy, torch, opencv-python-headless
2. 加载预训练模型:
```python
import torch
from models.matching import Matching
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = Matching(cfg=None)
model = model.eval().to(device)
model.load_state_dict(torch.load('checkpoints/loftr.pth')['state_dict'])
```
3. 加载图片并提取局部特征:
```python
import cv2
from models.utils import image_normalize
img0 = cv2.imread('img0.png', cv2.IMREAD_GRAYSCALE)
img1 = cv2.imread('img1.png', cv2.IMREAD_GRAYSCALE)
img0, img1 = image_normalize(img0, img1)
kpts0, desc0 = model.detect(img0.to(device))
kpts1, desc1 = model.detect(img1.to(device))
```
4. 进行特征匹配:
```python
from models.utils import knn_matcher
matcher = knn_matcher(k=2)
matches = matcher(desc0, desc1)
```
5. 获取匹配点对的坐标:
```python
import numpy as np
pts0 = kpts0[matches[:, 0], :2].cpu().numpy()
pts1 = kpts1[matches[:, 1], :2].cpu().numpy()
```
以上就是LoFTR算法Python代码实现的基本步骤。如果您想了解更多细节,请参考LoFTR的官方源码。
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