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import bisect
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
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'] # '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)
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