File size: 11,268 Bytes
e73df10
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8b973ee
 
 
 
 
 
e73df10
 
 
 
8b973ee
e73df10
8b973ee
 
 
e73df10
8b973ee
 
 
 
 
 
e73df10
8b973ee
 
 
 
 
e73df10
 
8b973ee
 
 
 
 
 
e73df10
8b973ee
 
 
 
 
e73df10
8b973ee
 
 
 
 
e73df10
8b973ee
 
 
 
 
 
 
 
e73df10
 
8b973ee
 
 
 
 
e73df10
8b973ee
 
 
 
 
 
e73df10
8b973ee
 
 
 
 
e73df10
8b973ee
 
 
 
 
e73df10
8b973ee
 
 
 
 
e73df10
8b973ee
 
e73df10
 
8b973ee
 
e73df10
8b973ee
 
 
 
e73df10
8b973ee
 
e73df10
 
 
 
 
 
 
8b973ee
e73df10
8b973ee
e73df10
8b973ee
e73df10
8b973ee
e73df10
8b973ee
 
e73df10
8b973ee
 
 
 
 
 
 
 
 
e73df10
 
 
8b973ee
e73df10
8b973ee
e73df10
8b973ee
e73df10
 
8b973ee
 
 
e73df10
 
 
 
8b973ee
e73df10
 
 
 
8b973ee
 
e73df10
8b973ee
e73df10
 
8b973ee
 
 
 
 
 
 
 
e73df10
 
 
 
 
 
8b973ee
e73df10
8b973ee
e73df10
 
 
8b973ee
 
 
 
 
 
e73df10
 
 
 
 
 
8b973ee
 
 
e73df10
8b973ee
 
e73df10
8b973ee
 
 
e73df10
 
8b973ee
 
 
 
 
 
 
 
 
 
 
 
e73df10
 
8b973ee
e73df10
8b973ee
e73df10
8b973ee
e73df10
8b973ee
 
 
e73df10
8b973ee
 
e73df10
8b973ee
 
 
 
 
 
 
 
 
 
 
 
 
 
e73df10
8b973ee
 
 
 
 
 
 
 
 
 
e73df10
 
8b973ee
e73df10
 
 
8b973ee
 
 
 
e73df10
 
 
 
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
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
#! /usr/bin/env python3
#
# %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
#                       Daniel DeTone
#                       Tomasz Malisiewicz
#
# %AUTHORS_END%
# --------------------------------------------------------------------*/
# %BANNER_END%

from pathlib import Path
import argparse
import cv2
import matplotlib.cm as cm
import torch

from models.matching import Matching
from models.utils import (
    AverageTimer,
    VideoStreamer,
    make_matching_plot_fast,
    frame2tensor,
)

torch.set_grad_enabled(False)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="SuperGlue demo",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument(
        "--input",
        type=str,
        default="0",
        help="ID of a USB webcam, URL of an IP camera, "
        "or path to an image directory or movie file",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default=None,
        help="Directory where to write output frames (If None, no output)",
    )

    parser.add_argument(
        "--image_glob",
        type=str,
        nargs="+",
        default=["*.png", "*.jpg", "*.jpeg"],
        help="Glob if a directory of images is specified",
    )
    parser.add_argument(
        "--skip",
        type=int,
        default=1,
        help="Images to skip if input is a movie or directory",
    )
    parser.add_argument(
        "--max_length",
        type=int,
        default=1000000,
        help="Maximum length if input is a movie or directory",
    )
    parser.add_argument(
        "--resize",
        type=int,
        nargs="+",
        default=[640, 480],
        help="Resize the input image before running inference. If two numbers, "
        "resize to the exact dimensions, if one number, resize the max "
        "dimension, if -1, do not resize",
    )

    parser.add_argument(
        "--superglue",
        choices={"indoor", "outdoor"},
        default="indoor",
        help="SuperGlue weights",
    )
    parser.add_argument(
        "--max_keypoints",
        type=int,
        default=-1,
        help="Maximum number of keypoints detected by Superpoint"
        " ('-1' keeps all keypoints)",
    )
    parser.add_argument(
        "--keypoint_threshold",
        type=float,
        default=0.005,
        help="SuperPoint keypoint detector confidence threshold",
    )
    parser.add_argument(
        "--nms_radius",
        type=int,
        default=4,
        help="SuperPoint Non Maximum Suppression (NMS) radius" " (Must be positive)",
    )
    parser.add_argument(
        "--sinkhorn_iterations",
        type=int,
        default=20,
        help="Number of Sinkhorn iterations performed by SuperGlue",
    )
    parser.add_argument(
        "--match_threshold", type=float, default=0.2, help="SuperGlue match threshold"
    )

    parser.add_argument(
        "--show_keypoints", action="store_true", help="Show the detected keypoints"
    )
    parser.add_argument(
        "--no_display",
        action="store_true",
        help="Do not display images to screen. Useful if running remotely",
    )
    parser.add_argument(
        "--force_cpu", action="store_true", help="Force pytorch to run in CPU mode."
    )

    opt = parser.parse_args()
    print(opt)

    if len(opt.resize) == 2 and opt.resize[1] == -1:
        opt.resize = opt.resize[0:1]
    if len(opt.resize) == 2:
        print("Will resize to {}x{} (WxH)".format(opt.resize[0], opt.resize[1]))
    elif len(opt.resize) == 1 and opt.resize[0] > 0:
        print("Will resize max dimension to {}".format(opt.resize[0]))
    elif len(opt.resize) == 1:
        print("Will not resize images")
    else:
        raise ValueError("Cannot specify more than two integers for --resize")

    device = "cuda" if torch.cuda.is_available() and not opt.force_cpu else "cpu"
    print('Running inference on device "{}"'.format(device))
    config = {
        "superpoint": {
            "nms_radius": opt.nms_radius,
            "keypoint_threshold": opt.keypoint_threshold,
            "max_keypoints": opt.max_keypoints,
        },
        "superglue": {
            "weights": opt.superglue,
            "sinkhorn_iterations": opt.sinkhorn_iterations,
            "match_threshold": opt.match_threshold,
        },
    }
    matching = Matching(config).eval().to(device)
    keys = ["keypoints", "scores", "descriptors"]

    vs = VideoStreamer(opt.input, opt.resize, opt.skip, opt.image_glob, opt.max_length)
    frame, ret = vs.next_frame()
    assert ret, "Error when reading the first frame (try different --input?)"

    frame_tensor = frame2tensor(frame, device)
    last_data = matching.superpoint({"image": frame_tensor})
    last_data = {k + "0": last_data[k] for k in keys}
    last_data["image0"] = frame_tensor
    last_frame = frame
    last_image_id = 0

    if opt.output_dir is not None:
        print("==> Will write outputs to {}".format(opt.output_dir))
        Path(opt.output_dir).mkdir(exist_ok=True)

    # Create a window to display the demo.
    if not opt.no_display:
        cv2.namedWindow("SuperGlue matches", cv2.WINDOW_NORMAL)
        cv2.resizeWindow("SuperGlue matches", 640 * 2, 480)
    else:
        print("Skipping visualization, will not show a GUI.")

    # Print the keyboard help menu.
    print(
        "==> Keyboard control:\n"
        "\tn: select the current frame as the anchor\n"
        "\te/r: increase/decrease the keypoint confidence threshold\n"
        "\td/f: increase/decrease the match filtering threshold\n"
        "\tk: toggle the visualization of keypoints\n"
        "\tq: quit"
    )

    timer = AverageTimer()

    while True:
        frame, ret = vs.next_frame()
        if not ret:
            print("Finished demo_superglue.py")
            break
        timer.update("data")
        stem0, stem1 = last_image_id, vs.i - 1

        frame_tensor = frame2tensor(frame, device)
        pred = matching({**last_data, "image1": frame_tensor})
        kpts0 = last_data["keypoints0"][0].cpu().numpy()
        kpts1 = pred["keypoints1"][0].cpu().numpy()
        matches = pred["matches0"][0].cpu().numpy()
        confidence = pred["matching_scores0"][0].cpu().numpy()
        timer.update("forward")

        valid = matches > -1
        mkpts0 = kpts0[valid]
        mkpts1 = kpts1[matches[valid]]
        color = cm.jet(confidence[valid])
        text = [
            "SuperGlue",
            "Keypoints: {}:{}".format(len(kpts0), len(kpts1)),
            "Matches: {}".format(len(mkpts0)),
        ]
        k_thresh = matching.superpoint.config["keypoint_threshold"]
        m_thresh = matching.superglue.config["match_threshold"]
        small_text = [
            "Keypoint Threshold: {:.4f}".format(k_thresh),
            "Match Threshold: {:.2f}".format(m_thresh),
            "Image Pair: {:06}:{:06}".format(stem0, stem1),
        ]
        out = make_matching_plot_fast(
            last_frame,
            frame,
            kpts0,
            kpts1,
            mkpts0,
            mkpts1,
            color,
            text,
            path=None,
            show_keypoints=opt.show_keypoints,
            small_text=small_text,
        )

        if not opt.no_display:
            cv2.imshow("SuperGlue matches", out)
            key = chr(cv2.waitKey(1) & 0xFF)
            if key == "q":
                vs.cleanup()
                print("Exiting (via q) demo_superglue.py")
                break
            elif key == "n":  # set the current frame as anchor
                last_data = {k + "0": pred[k + "1"] for k in keys}
                last_data["image0"] = frame_tensor
                last_frame = frame
                last_image_id = vs.i - 1
            elif key in ["e", "r"]:
                # Increase/decrease keypoint threshold by 10% each keypress.
                d = 0.1 * (-1 if key == "e" else 1)
                matching.superpoint.config["keypoint_threshold"] = min(
                    max(
                        0.0001,
                        matching.superpoint.config["keypoint_threshold"] * (1 + d),
                    ),
                    1,
                )
                print(
                    "\nChanged the keypoint threshold to {:.4f}".format(
                        matching.superpoint.config["keypoint_threshold"]
                    )
                )
            elif key in ["d", "f"]:
                # Increase/decrease match threshold by 0.05 each keypress.
                d = 0.05 * (-1 if key == "d" else 1)
                matching.superglue.config["match_threshold"] = min(
                    max(0.05, matching.superglue.config["match_threshold"] + d), 0.95
                )
                print(
                    "\nChanged the match threshold to {:.2f}".format(
                        matching.superglue.config["match_threshold"]
                    )
                )
            elif key == "k":
                opt.show_keypoints = not opt.show_keypoints

        timer.update("viz")
        timer.print()

        if opt.output_dir is not None:
            # stem = 'matches_{:06}_{:06}'.format(last_image_id, vs.i-1)
            stem = "matches_{:06}_{:06}".format(stem0, stem1)
            out_file = str(Path(opt.output_dir, stem + ".png"))
            print("\nWriting image to {}".format(out_file))
            cv2.imwrite(out_file, out)

    cv2.destroyAllWindows()
    vs.cleanup()