1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200

# %BANNER_BEGIN%
# ---------------------------------------------------------------------
# %COPYRIGHT_BEGIN%
#
#  Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
#
#  Unpublished Copyright (c) 2020
#  Magic Leap, Inc., All Rights Reserved.
#
# NOTICE:  All information contained herein is, and remains the property
# of COMPANY. The intellectual and technical concepts contained herein
# are proprietary to COMPANY and may be covered by U.S. and Foreign
# Patents, patents in process, and are protected by trade secret or
# copyright law.  Dissemination of this information or reproduction of
# this material is strictly forbidden unless prior written permission is
# obtained from COMPANY.  Access to the source code contained herein is
# hereby forbidden to anyone except current COMPANY employees, managers
# or contractors who have executed Confidentiality and Non-disclosure
# agreements explicitly covering such access.
#
# The copyright notice above does not evidence any actual or intended
# publication or disclosure  of  this source code, which includes
# information that is confidential and/or proprietary, and is a trade
# secret, of  COMPANY.   ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
# PUBLIC  PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE  OF THIS
# SOURCE CODE  WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS
# STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND
# INTERNATIONAL TREATIES.  THE RECEIPT OR POSSESSION OF  THIS SOURCE
# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS
# TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE,
# USE, OR SELL ANYTHING THAT IT  MAY DESCRIBE, IN WHOLE OR IN PART.
#
# %COPYRIGHT_END%
# ----------------------------------------------------------------------
# %AUTHORS_BEGIN%
#
#  Originating Authors: Paul-Edouard Sarlin
#
# %AUTHORS_END%
# --------------------------------------------------------------------*/
# %BANNER_END%
from pathlib import Path
import torch
from torch import nn
def simple_nms(scores, nms_radius: int):
    """ Fast Non-maximum suppression to remove nearby points """
    assert(nms_radius >= 0)
    def max_pool(x):
        return torch.nn.functional.max_pool2d(
            x, kernel_size=nms_radius*2+1, stride=1, padding=nms_radius)
    zeros = torch.zeros_like(scores)
    max_mask = scores == max_pool(scores)
    for _ in range(2):
        supp_mask = max_pool(max_mask.float()) > 0
        supp_scores = torch.where(supp_mask, zeros, scores)
        new_max_mask = supp_scores == max_pool(supp_scores)
        max_mask = max_mask | (new_max_mask & (~supp_mask))
    return torch.where(max_mask, scores, zeros)
def remove_borders(keypoints, scores, border: int, height: int, width: int):
    """ Removes keypoints too close to the border """
    mask_h = (keypoints[:, 0] >= border) & (keypoints[:, 0] < (height - border))
    mask_w = (keypoints[:, 1] >= border) & (keypoints[:, 1] < (width - border))
    mask = mask_h & mask_w
    return keypoints[mask], scores[mask]
def top_k_keypoints(keypoints, scores, k: int):
    if k >= len(keypoints):
        return keypoints, scores
    scores, indices = torch.topk(scores, k, dim=0)
    return keypoints[indices], scores
def sample_descriptors(keypoints, descriptors, s: int = 8):
    """ Interpolate descriptors at keypoint locations """
    b, c, h, w = descriptors.shape
    keypoints = keypoints - s / 2 + 0.5
    keypoints /= torch.tensor([(w*s - s/2 - 0.5), (h*s - s/2 - 0.5)],
                              ).to(keypoints)[None]
    keypoints = keypoints*2 - 1  # normalize to (-1, 1)
    args = {'align_corners': True} if torch.__version__ >= '1.3' else {}
    descriptors = torch.nn.functional.grid_sample(
        descriptors, keypoints.view(b, 1, -1, 2), mode='bilinear', **args)
    descriptors = torch.nn.functional.normalize(
        descriptors.reshape(b, c, -1), p=2, dim=1)
    return descriptors
class SuperPoint(nn.Module):
    """SuperPoint Convolutional Detector and Descriptor
    SuperPoint: Self-Supervised Interest Point Detection and
    Description. Daniel DeTone, Tomasz Malisiewicz, and Andrew
    Rabinovich. In CVPRW, 2019. https://arxiv.org/abs/1712.07629
    """
    default_config = {
        'descriptor_dim': 256,
        'nms_radius': 4,
        'keypoint_threshold': 0.005,
        'max_keypoints': -1,
        'remove_borders': 4,
    }
    def __init__(self, config):
        super().__init__()
        self.config = {**self.default_config, **config}
        self.relu = nn.ReLU(inplace=True)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        c1, c2, c3, c4, c5 = 64, 64, 128, 128, 256
        self.conv1a = nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1)
        self.conv1b = nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1)
        self.conv2a = nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1)
        self.conv2b = nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1)
        self.conv3a = nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1)
        self.conv3b = nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1)
        self.conv4a = nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1)
        self.conv4b = nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1)
        self.convPa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1)
        self.convPb = nn.Conv2d(c5, 65, kernel_size=1, stride=1, padding=0)
        self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1)
        self.convDb = nn.Conv2d(
            c5, self.config['descriptor_dim'],
            kernel_size=1, stride=1, padding=0)
        path = Path(__file__).parent / 'weights/superpoint_v1.pth'
        self.load_state_dict(torch.load(str(path)))
        mk = self.config['max_keypoints']
        if mk == 0 or mk < -1:
            raise ValueError('\"max_keypoints\" must be positive or \"-1\"')
        print('Loaded SuperPoint model')
    def forward(self, data):
        """ Compute keypoints, scores, descriptors for image """
        # Shared Encoder
        x = self.relu(self.conv1a(data['image']))
        x = self.relu(self.conv1b(x))
        x = self.pool(x)
        x = self.relu(self.conv2a(x))
        x = self.relu(self.conv2b(x))
        x = self.pool(x)
        x = self.relu(self.conv3a(x))
        x = self.relu(self.conv3b(x))
        x = self.pool(x)
        x = self.relu(self.conv4a(x))
        x = self.relu(self.conv4b(x))
        # Compute the dense keypoint scores
        cPa = self.relu(self.convPa(x))
        scores = self.convPb(cPa)
        scores = torch.nn.functional.softmax(scores, 1)[:, :-1]
        b, _, h, w = scores.shape
        scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, 8, 8)
        scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h*8, w*8)
        scores = simple_nms(scores, self.config['nms_radius'])
        # Extract keypoints
        keypoints = [
            torch.nonzero(s > self.config['keypoint_threshold'])
            for s in scores]
        scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)]
        # Discard keypoints near the image borders
        keypoints, scores = list(zip(*[
            remove_borders(k, s, self.config['remove_borders'], h*8, w*8)
            for k, s in zip(keypoints, scores)]))
        # Keep the k keypoints with highest score
        if self.config['max_keypoints'] >= 0:
            keypoints, scores = list(zip(*[
                top_k_keypoints(k, s, self.config['max_keypoints'])
                for k, s in zip(keypoints, scores)]))
        # Convert (h, w) to (x, y)
        keypoints = [torch.flip(k, [1]).float() for k in keypoints]
        # Compute the dense descriptors
        cDa = self.relu(self.convDa(x))
        descriptors = self.convDb(cDa)
        descriptors = torch.nn.functional.normalize(descriptors, p=2, dim=1)
        # Extract descriptors
        descriptors = [sample_descriptors(k[None], d[None], 8)[0]
                       for k, d in zip(keypoints, descriptors)]
        return {
            'keypoints': keypoints,
            'scores': scores,
            'descriptors': descriptors,
        }