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# %BANNER_BEGIN%
# ---------------------------------------------------------------------
# %COPYRIGHT_BEGIN%
#
# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
#
# Unpublished Copyright (c) 2020
# Magic Leap, Inc., All Rights Reserved.
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# copyright law. Dissemination of this information or reproduction of
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# %AUTHORS_BEGIN%
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# Originating Authors: Paul-Edouard Sarlin
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# %BANNER_END%
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
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