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#! /usr/bin/env python3
#
# %BANNER_BEGIN%
# ---------------------------------------------------------------------
# %COPYRIGHT_BEGIN%
#
# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
#
# Unpublished Copyright (c) 2020
# Magic Leap, Inc., All Rights Reserved.
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# of COMPANY. The intellectual and technical concepts contained herein
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# Patents, patents in process, and are protected by trade secret or
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# obtained from COMPANY. Access to the source code contained herein is
# hereby forbidden to anyone except current COMPANY employees, managers
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# secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
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# ----------------------------------------------------------------------
# %AUTHORS_BEGIN%
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# Originating Authors: Paul-Edouard Sarlin
# Daniel DeTone
# Tomasz Malisiewicz
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# %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), .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()
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