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#! /usr/bin/env python3
#
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# %AUTHORS_BEGIN%
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# Originating Authors: Paul-Edouard Sarlin
# Daniel DeTone
# Tomasz Malisiewicz
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from pathlib import Path
import argparse
import random
import numpy as np
import matplotlib.cm as cm
import torch
from models.matching import Matching
from models.utils import (
compute_pose_error,
compute_epipolar_error,
estimate_pose,
make_matching_plot,
error_colormap,
AverageTimer,
pose_auc,
read_image,
rotate_intrinsics,
rotate_pose_inplane,
scale_intrinsics,
)
torch.set_grad_enabled(False)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Image pair matching and pose evaluation with SuperGlue",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--input_pairs",
type=str,
default="assets/scannet_sample_pairs_with_gt.txt",
help="Path to the list of image pairs",
)
parser.add_argument(
"--input_dir",
type=str,
default="assets/scannet_sample_images/",
help="Path to the directory that contains the images",
)
parser.add_argument(
"--output_dir",
type=str,
default="dump_match_pairs/",
help="Path to the directory in which the .npz results and optionally,"
"the visualization images are written",
)
parser.add_argument(
"--max_length", type=int, default=-1, help="Maximum number of pairs to evaluate"
)
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(
"--resize_float",
action="store_true",
help="Resize the image after casting uint8 to float",
)
parser.add_argument(
"--superglue",
choices={"indoor", "outdoor"},
default="indoor",
help="SuperGlue weights",
)
parser.add_argument(
"--max_keypoints",
type=int,
default=1024,
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(
"--viz", action="store_true", help="Visualize the matches and dump the plots"
)
parser.add_argument(
"--eval",
action="store_true",
help="Perform the evaluation" " (requires ground truth pose and intrinsics)",
)
parser.add_argument(
"--fast_viz",
action="store_true",
help="Use faster image visualization with OpenCV instead of Matplotlib",
)
parser.add_argument(
"--cache",
action="store_true",
help="Skip the pair if output .npz files are already found",
)
parser.add_argument(
"--show_keypoints",
action="store_true",
help="Plot the keypoints in addition to the matches",
)
parser.add_argument(
"--viz_extension",
type=str,
default="png",
choices=["png", "pdf"],
help="Visualization file extension. Use pdf for highest-quality.",
)
parser.add_argument(
"--opencv_display",
action="store_true",
help="Visualize via OpenCV before saving output images",
)
parser.add_argument(
"--shuffle",
action="store_true",
help="Shuffle ordering of pairs before processing",
)
parser.add_argument(
"--force_cpu", action="store_true", help="Force pytorch to run in CPU mode."
)
opt = parser.parse_args()
print(opt)
assert not (
opt.opencv_display and not opt.viz
), "Must use --viz with --opencv_display"
assert not (
opt.opencv_display and not opt.fast_viz
), "Cannot use --opencv_display without --fast_viz"
assert not (opt.fast_viz and not opt.viz), "Must use --viz with --fast_viz"
assert not (
opt.fast_viz and opt.viz_extension == "pdf"
), "Cannot use pdf extension with --fast_viz"
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")
with open(opt.input_pairs, "r") as f:
pairs = [l.split() for l in f.readlines()]
if opt.max_length > -1:
pairs = pairs[0 : np.min([len(pairs), opt.max_length])]
if opt.shuffle:
random.Random(0).shuffle(pairs)
if opt.eval:
if not all([len(p) == 38 for p in pairs]):
raise ValueError(
"All pairs should have ground truth info for evaluation."
'File "{}" needs 38 valid entries per row'.format(opt.input_pairs)
)
# Load the SuperPoint and SuperGlue models.
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)
# Create the output directories if they do not exist already.
input_dir = Path(opt.input_dir)
print('Looking for data in directory "{}"'.format(input_dir))
output_dir = Path(opt.output_dir)
output_dir.mkdir(exist_ok=True, parents=True)
print('Will write matches to directory "{}"'.format(output_dir))
if opt.eval:
print("Will write evaluation results", 'to directory "{}"'.format(output_dir))
if opt.viz:
print("Will write visualization images to", 'directory "{}"'.format(output_dir))
timer = AverageTimer(newline=True)
for i, pair in enumerate(pairs):
name0, name1 = pair[:2]
stem0, stem1 = Path(name0).stem, Path(name1).stem
matches_path = output_dir / "{}_{}_matches.npz".format(stem0, stem1)
eval_path = output_dir / "{}_{}_evaluation.npz".format(stem0, stem1)
viz_path = output_dir / "{}_{}_matches.{}".format(
stem0, stem1, opt.viz_extension
)
viz_eval_path = output_dir / "{}_{}_evaluation.{}".format(
stem0, stem1, opt.viz_extension
)
# Handle --cache logic.
do_match = True
do_eval = opt.eval
do_viz = opt.viz
do_viz_eval = opt.eval and opt.viz
if opt.cache:
if matches_path.exists():
try:
results = np.load(matches_path)
except:
raise IOError("Cannot load matches .npz file: %s" % matches_path)
kpts0, kpts1 = results["keypoints0"], results["keypoints1"]
matches, conf = results["matches"], results["match_confidence"]
do_match = False
if opt.eval and eval_path.exists():
try:
results = np.load(eval_path)
except:
raise IOError("Cannot load eval .npz file: %s" % eval_path)
err_R, err_t = results["error_R"], results["error_t"]
precision = results["precision"]
matching_score = results["matching_score"]
num_correct = results["num_correct"]
epi_errs = results["epipolar_errors"]
do_eval = False
if opt.viz and viz_path.exists():
do_viz = False
if opt.viz and opt.eval and viz_eval_path.exists():
do_viz_eval = False
timer.update("load_cache")
if not (do_match or do_eval or do_viz or do_viz_eval):
timer.print("Finished pair {:5} of {:5}".format(i, len(pairs)))
continue
# If a rotation integer is provided (e.g. from EXIF data), use it:
if len(pair) >= 5:
rot0, rot1 = int(pair[2]), int(pair[3])
else:
rot0, rot1 = 0, 0
# Load the image pair.
image0, inp0, scales0 = read_image(
input_dir / name0, device, opt.resize, rot0, opt.resize_float
)
image1, inp1, scales1 = read_image(
input_dir / name1, device, opt.resize, rot1, opt.resize_float
)
if image0 is None or image1 is None:
print(
"Problem reading image pair: {} {}".format(
input_dir / name0, input_dir / name1
)
)
exit(1)
timer.update("load_image")
if do_match:
# Perform the matching.
pred = matching({"image0": inp0, "image1": inp1})
pred = {k: v[0].cpu().numpy() for k, v in pred.items()}
kpts0, kpts1 = pred["keypoints0"], pred["keypoints1"]
matches, conf = pred["matches0"], pred["matching_scores0"]
timer.update("matcher")
# Write the matches to disk.
out_matches = {
"keypoints0": kpts0,
"keypoints1": kpts1,
"matches": matches,
"match_confidence": conf,
}
np.savez(str(matches_path), **out_matches)
# Keep the matching keypoints.
valid = matches > -1
mkpts0 = kpts0[valid]
mkpts1 = kpts1[matches[valid]]
mconf = conf[valid]
if do_eval:
# Estimate the pose and compute the pose error.
assert len(pair) == 38, "Pair does not have ground truth info"
K0 = np.array(pair[4:13]).astype(float).reshape(3, 3)
K1 = np.array(pair[13:22]).astype(float).reshape(3, 3)
T_0to1 = np.array(pair[22:]).astype(float).reshape(4, 4)
# Scale the intrinsics to resized image.
K0 = scale_intrinsics(K0, scales0)
K1 = scale_intrinsics(K1, scales1)
# Update the intrinsics + extrinsics if EXIF rotation was found.
if rot0 != 0 or rot1 != 0:
cam0_T_w = np.eye(4)
cam1_T_w = T_0to1
if rot0 != 0:
K0 = rotate_intrinsics(K0, image0.shape, rot0)
cam0_T_w = rotate_pose_inplane(cam0_T_w, rot0)
if rot1 != 0:
K1 = rotate_intrinsics(K1, image1.shape, rot1)
cam1_T_w = rotate_pose_inplane(cam1_T_w, rot1)
cam1_T_cam0 = cam1_T_w @ np.linalg.inv(cam0_T_w)
T_0to1 = cam1_T_cam0
epi_errs = compute_epipolar_error(mkpts0, mkpts1, T_0to1, K0, K1)
correct = epi_errs < 5e-4
num_correct = np.sum(correct)
precision = np.mean(correct) if len(correct) > 0 else 0
matching_score = num_correct / len(kpts0) if len(kpts0) > 0 else 0
thresh = 1.0 # In pixels relative to resized image size.
ret = estimate_pose(mkpts0, mkpts1, K0, K1, thresh)
if ret is None:
err_t, err_R = np.inf, np.inf
else:
R, t, inliers = ret
err_t, err_R = compute_pose_error(T_0to1, R, t)
# Write the evaluation results to disk.
out_eval = {
"error_t": err_t,
"error_R": err_R,
"precision": precision,
"matching_score": matching_score,
"num_correct": num_correct,
"epipolar_errors": epi_errs,
}
np.savez(str(eval_path), **out_eval)
timer.update("eval")
if do_viz:
# Visualize the matches.
color = cm.jet(mconf)
text = [
"SuperGlue",
"Keypoints: {}:{}".format(len(kpts0), len(kpts1)),
"Matches: {}".format(len(mkpts0)),
]
if rot0 != 0 or rot1 != 0:
text.append("Rotation: {}:{}".format(rot0, rot1))
# Display extra parameter info.
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: {}:{}".format(stem0, stem1),
]
make_matching_plot(
image0,
image1,
kpts0,
kpts1,
mkpts0,
mkpts1,
color,
text,
viz_path,
opt.show_keypoints,
opt.fast_viz,
opt.opencv_display,
"Matches",
small_text,
)
timer.update("viz_match")
if do_viz_eval:
# Visualize the evaluation results for the image pair.
color = np.clip((epi_errs - 0) / (1e-3 - 0), 0, 1)
color = error_colormap(1 - color)
deg, delta = " deg", "Delta "
if not opt.fast_viz:
deg, delta = "°", "$\\Delta$"
e_t = "FAIL" if np.isinf(err_t) else "{:.1f}{}".format(err_t, deg)
e_R = "FAIL" if np.isinf(err_R) else "{:.1f}{}".format(err_R, deg)
text = [
"SuperGlue",
"{}R: {}".format(delta, e_R),
"{}t: {}".format(delta, e_t),
"inliers: {}/{}".format(num_correct, (matches > -1).sum()),
]
if rot0 != 0 or rot1 != 0:
text.append("Rotation: {}:{}".format(rot0, rot1))
# Display extra parameter info (only works with --fast_viz).
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: {}:{}".format(stem0, stem1),
]
make_matching_plot(
image0,
image1,
kpts0,
kpts1,
mkpts0,
mkpts1,
color,
text,
viz_eval_path,
opt.show_keypoints,
opt.fast_viz,
opt.opencv_display,
"Relative Pose",
small_text,
)
timer.update("viz_eval")
timer.print("Finished pair {:5} of {:5}".format(i, len(pairs)))
if opt.eval:
# Collate the results into a final table and print to terminal.
pose_errors = []
precisions = []
matching_scores = []
for pair in pairs:
name0, name1 = pair[:2]
stem0, stem1 = Path(name0).stem, Path(name1).stem
eval_path = output_dir / "{}_{}_evaluation.npz".format(stem0, stem1)
results = np.load(eval_path)
pose_error = np.maximum(results["error_t"], results["error_R"])
pose_errors.append(pose_error)
precisions.append(results["precision"])
matching_scores.append(results["matching_score"])
thresholds = [5, 10, 20]
aucs = pose_auc(pose_errors, thresholds)
aucs = [100.0 * yy for yy in aucs]
prec = 100.0 * np.mean(precisions)
ms = 100.0 * np.mean(matching_scores)
print("Evaluation Results (mean over {} pairs):".format(len(pairs)))
print("AUC@5\t AUC@10\t AUC@20\t Prec\t MScore\t")
print(
"{:.2f}\t {:.2f}\t {:.2f}\t {:.2f}\t {:.2f}\t".format(
aucs[0], aucs[1], aucs[2], prec, ms
)
)