详细解释一下这段代码，每一句给出详细注解：def superglue_inference(model, cache, fname1, fname2, image_size): # SuperPoint if 'keypoints' not in cache[fname1][image_size]: with torch.no_grad(): pred = model.superpoint({'image': cache[fname1][image_size]['img']}) cache[fname1][image_size] = {**cache[fname1][image_size], **{'keypoints': torch.stack(pred['keypoints']), 'scores': torch.stack(pred['scores']), 'descriptors': torch.stack(pred['descriptors'])}} if 'keypoints' not in cache[fname2][image_size]: with torch.no_grad(): pred = model.superpoint({'image': cache[fname2][image_size]['img']}) cache[fname2][image_size] = {**cache[fname2][image_size], **{'keypoints': torch.stack(pred['keypoints']), 'scores': torch.stack(pred['scores']), 'descriptors': torch.stack(pred['descriptors'])}} # SuperGlue with torch.no_grad(): data = { 'image0': cache[fname1][image_size]['img'], 'image1': cache[fname2][image_size]['img'], 'keypoints0': cache[fname1][image_size]['keypoints'], 'keypoints1': cache[fname2][image_size]['keypoints'], 'scores0': cache[fname1][image_size]['scores'], 'scores1': cache[fname2][image_size]['scores'], 'descriptors0': cache[fname1][image_size]['descriptors'], 'descriptors1': cache[fname2][image_size]['descriptors'] } pred = model.superglue(data) kpts1, kpts2 = cache[fname1][image_size]['keypoints'][0].cpu().numpy(), cache[fname2][image_size]['keypoints'][0].cpu().numpy() matches = pred['matches0'][0].cpu().numpy() valid_matches = matches > -1 mkpts1 = kpts1[valid_matches].astype(np.float32) mkpts2 = kpts2[matches[valid_matches]].astype(np.float32) return mkpts1, mkpts2

这段代码实现了使用 SuperPoint 和 SuperGlue 算法进行图像匹配的功能。下面是每一句的注释：

def superglue_inference(model, cache, fname1, fname2, image_size):

定义一个名为 superglue_inference 的函数，它接受五个参数：model，cache，fname1，fname2 和 image_size。

# SuperPoint
if 'keypoints' not in cache[fname1][image_size]:
    with torch.no_grad():
        pred = model.superpoint({'image': cache[fname1][image_size]['img']})
    cache[fname1][image_size] = {**cache[fname1][image_size], **{'keypoints': torch.stack(pred['keypoints']), 'scores': torch.stack(pred['scores']), 'descriptors': torch.stack(pred['descriptors'])}}

如果在 cache[fname1][image_size] 中没有 keypoints，则使用 SuperPoint 模型（model.superpoint）检测图像中的关键点、分数和描述符，并将它们存储在 cache[fname1][image_size] 中。

if 'keypoints' not in cache[fname2][image_size]:
    with torch.no_grad():
        pred = model.superpoint({'image': cache[fname2][image_size]['img']})
    cache[fname2][image_size] = {**cache[fname2][image_size], **{'keypoints': torch.stack(pred['keypoints']), 'scores': torch.stack(pred['scores']), 'descriptors': torch.stack(pred['descriptors'])}}

如果在 cache[fname2][image_size] 中没有 keypoints，则使用 SuperPoint 模型检测图像中的关键点、分数和描述符，并将它们存储在 cache[fname2][image_size] 中。

# SuperGlue
with torch.no_grad():
    data = {
        'image0': cache[fname1][image_size]['img'],
        'image1': cache[fname2][image_size]['img'],
        'keypoints0': cache[fname1][image_size]['keypoints'],
        'keypoints1': cache[fname2][image_size]['keypoints'],
        'scores0': cache[fname1][image_size]['scores'],
        'scores1': cache[fname2][image_size]['scores'],
        'descriptors0': cache[fname1][image_size]['descriptors'],
        'descriptors1': cache[fname2][image_size]['descriptors']
    }
    pred = model.superglue(data)

使用 SuperGlue 模型（model.superglue）对两幅图像进行匹配。将两幅图像、两幅图像中的关键点、分数和描述符作为输入数据（data），并将输出结果存储在 pred 中。

kpts1, kpts2 = cache[fname1][image_size]['keypoints'][0].cpu().numpy(), cache[fname2][image_size]['keypoints'][0].cpu().numpy()
matches = pred['matches0'][0].cpu().numpy()
valid_matches = matches > -1
mkpts1 = kpts1[valid_matches].astype(np.float32)
mkpts2 = kpts2[matches[valid_matches]].astype(np.float32)

从 cache[fname1][image_size] 和 cache[fname2][image_size] 中获取关键点，将其转换为 NumPy 数组，并从 pred 中获取匹配。接着，将 matches 中大于 -1 的匹配视为有效匹配，并分别将两幅图像的关键点存储在 mkpts1 和 mkpts2 中。

return mkpts1, mkpts2

返回 mkpts1 和 mkpts2，即两幅图像之间的有效匹配。