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| # Originating Authors: Paul-Edouard Sarlin | |
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| import torch | |
| from .superpoint import SuperPoint | |
| from .superglue import SuperGlue | |
| class Matching(torch.nn.Module): | |
| """ Image Matching Frontend (SuperPoint + SuperGlue) """ | |
| def __init__(self, config={}): | |
| super().__init__() | |
| self.superpoint = SuperPoint(config.get('superpoint', {})) | |
| self.superglue = SuperGlue(config.get('superglue', {})) | |
| def forward(self, data): | |
| """ Run SuperPoint (optionally) and SuperGlue | |
| SuperPoint is skipped if ['keypoints0', 'keypoints1'] exist in input | |
| Args: | |
| data: dictionary with minimal keys: ['image0', 'image1'] | |
| """ | |
| pred = {} | |
| # Extract SuperPoint (keypoints, scores, descriptors) if not provided | |
| if 'keypoints0' not in data: | |
| pred0 = self.superpoint({'image': data['image0']}) | |
| pred = {**pred, **{k+'0': v for k, v in pred0.items()}} | |
| if 'keypoints1' not in data: | |
| pred1 = self.superpoint({'image': data['image1']}) | |
| pred = {**pred, **{k+'1': v for k, v in pred1.items()}} | |
| # Batch all features | |
| # We should either have i) one image per batch, or | |
| # ii) the same number of local features for all images in the batch. | |
| data = {**data, **pred} | |
| for k in data: | |
| if isinstance(data[k], (list, tuple)): | |
| data[k] = torch.stack(data[k]) | |
| # Perform the matching | |
| pred = {**pred, **self.superglue(data)} | |
| return pred | |