Spaces:
Running
Running
File size: 5,693 Bytes
e02ffe6 |
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 |
import bisect
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import torch
def _compute_conf_thresh(data):
dataset_name = data['dataset_name'][0].lower()
if dataset_name == 'scannet':
thr = 5e-4
elif dataset_name == 'megadepth':
thr = 1e-4
else:
raise ValueError(f'Unknown dataset: {dataset_name}')
return thr
# --- VISUALIZATION --- #
def make_matching_figure(
img0, img1, mkpts0, mkpts1, color,
kpts0=None, kpts1=None, text=[], dpi=75, path=None):
# draw image pair
assert mkpts0.shape[0] == mkpts1.shape[0], f'mkpts0: {mkpts0.shape[0]} v.s. mkpts1: {mkpts1.shape[0]}'
fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi)
axes[0].imshow(img0, cmap='gray')
axes[1].imshow(img1, cmap='gray')
for i in range(2): # clear all frames
axes[i].get_yaxis().set_ticks([])
axes[i].get_xaxis().set_ticks([])
for spine in axes[i].spines.values():
spine.set_visible(False)
plt.tight_layout(pad=1)
if kpts0 is not None:
assert kpts1 is not None
axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c='w', s=2)
axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c='w', s=2)
# draw matches
if mkpts0.shape[0] != 0 and mkpts1.shape[0] != 0:
fig.canvas.draw()
transFigure = fig.transFigure.inverted()
fkpts0 = transFigure.transform(axes[0].transData.transform(mkpts0))
fkpts1 = transFigure.transform(axes[1].transData.transform(mkpts1))
fig.lines = [matplotlib.lines.Line2D((fkpts0[i, 0], fkpts1[i, 0]),
(fkpts0[i, 1], fkpts1[i, 1]),
transform=fig.transFigure, c=color[i], linewidth=1)
for i in range(len(mkpts0))]
axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color, s=4)
axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color, s=4)
# put txts
txt_color = 'k' if img0[:100, :200].mean() > 200 else 'w'
fig.text(
0.01, 0.99, '\n'.join(text), transform=fig.axes[0].transAxes,
fontsize=15, va='top', ha='left', color=txt_color)
# save or return figure
if path:
plt.savefig(str(path), bbox_inches='tight', pad_inches=0)
plt.close()
else:
return fig
def _make_evaluation_figure(data, b_id, alpha='dynamic'):
b_mask = data['m_bids'] == b_id
conf_thr = _compute_conf_thresh(data)
img0 = (data['image0'][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
img1 = (data['image1'][b_id][0].cpu().numpy() * 255).round().astype(np.int32)
kpts0 = data['mkpts0_f'][b_mask].cpu().numpy()
kpts1 = data['mkpts1_f'][b_mask].cpu().numpy()
# for megadepth, we visualize matches on the resized image
if 'scale0' in data:
kpts0 = kpts0 / data['scale0'][b_id].cpu().numpy()[[1, 0]]
kpts1 = kpts1 / data['scale1'][b_id].cpu().numpy()[[1, 0]]
epi_errs = data['epi_errs'][b_mask].cpu().numpy()
correct_mask = epi_errs < conf_thr
precision = np.mean(correct_mask) if len(correct_mask) > 0 else 0
n_correct = np.sum(correct_mask)
n_gt_matches = int(data['conf_matrix_gt'][b_id].sum().cpu())
recall = 0 if n_gt_matches == 0 else n_correct / (n_gt_matches)
# recall might be larger than 1, since the calculation of conf_matrix_gt
# uses groundtruth depths and camera poses, but epipolar distance is used here.
# matching info
if alpha == 'dynamic':
alpha = dynamic_alpha(len(correct_mask))
color = error_colormap(epi_errs, conf_thr, alpha=alpha)
text = [
f'#Matches {len(kpts0)}',
f'Precision({conf_thr:.2e}) ({100 * precision:.1f}%): {n_correct}/{len(kpts0)}',
f'Recall({conf_thr:.2e}) ({100 * recall:.1f}%): {n_correct}/{n_gt_matches}'
]
# make the figure
figure = make_matching_figure(img0, img1, kpts0, kpts1,
color, text=text)
return figure
def _make_confidence_figure(data, b_id):
# TODO: Implement confidence figure
raise NotImplementedError()
def make_matching_figures(data, config, mode='evaluation'):
""" Make matching figures for a batch.
Args:
data (Dict): a batch updated by PL_LoFTR.
config (Dict): matcher config
Returns:
figures (Dict[str, List[plt.figure]]
"""
assert mode in ['evaluation', 'confidence', 'gt'] # 'confidence'
figures = {mode: []}
for b_id in range(data['image0'].size(0)):
if mode == 'evaluation':
fig = _make_evaluation_figure(
data, b_id,
alpha=config.TRAINER.PLOT_MATCHES_ALPHA)
elif mode == 'confidence':
fig = _make_confidence_figure(data, b_id)
else:
raise ValueError(f'Unknown plot mode: {mode}')
figures[mode].append(fig)
return figures
def dynamic_alpha(n_matches,
milestones=[0, 300, 1000, 2000],
alphas=[1.0, 0.8, 0.4, 0.2]):
if n_matches == 0:
return 1.0
ranges = list(zip(alphas, alphas[1:] + [None]))
loc = bisect.bisect_right(milestones, n_matches) - 1
_range = ranges[loc]
if _range[1] is None:
return _range[0]
return _range[1] + (milestones[loc + 1] - n_matches) / (
milestones[loc + 1] - milestones[loc]) * (_range[0] - _range[1])
def error_colormap(err, thr, alpha=1.0):
assert alpha <= 1.0 and alpha > 0, f"Invaid alpha value: {alpha}"
x = 1 - np.clip(err / (thr * 2), 0, 1)
return np.clip(
np.stack([2-x*2, x*2, np.zeros_like(x), np.ones_like(x)*alpha], -1), 0, 1) |