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import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
def plot_images(imgs, titles=None, cmaps="gray", dpi=100, size=5, pad=0.5):
"""Plot a set of images horizontally.
Args:
imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W).
titles: a list of strings, as titles for each image.
cmaps: colormaps for monochrome images.
"""
n = len(imgs)
if not isinstance(cmaps, (list, tuple)):
cmaps = [cmaps] * n
# figsize = (size*n, size*3/4) if size is not None else None
figsize = (size * n, size * 6 / 5) if size is not None else None
fig, ax = plt.subplots(1, n, figsize=figsize, dpi=dpi)
if n == 1:
ax = [ax]
for i in range(n):
ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmaps[i]))
ax[i].get_yaxis().set_ticks([])
ax[i].get_xaxis().set_ticks([])
ax[i].set_axis_off()
for spine in ax[i].spines.values(): # remove frame
spine.set_visible(False)
if titles:
ax[i].set_title(titles[i])
fig.tight_layout(pad=pad)
return fig
def plot_color_line_matches(lines, correct_matches=None, lw=2, indices=(0, 1)):
"""Plot line matches for existing images with multiple colors.
Args:
lines: list of ndarrays of size (N, 2, 2).
correct_matches: bool array of size (N,) indicating correct matches.
lw: line width as float pixels.
indices: indices of the images to draw the matches on.
"""
n_lines = len(lines[0])
colors = sns.color_palette("husl", n_colors=n_lines)
np.random.shuffle(colors)
alphas = np.ones(n_lines)
# If correct_matches is not None, display wrong matches with a low alpha
if correct_matches is not None:
alphas[~np.array(correct_matches)] = 0.2
fig = plt.gcf()
ax = fig.axes
assert len(ax) > max(indices)
axes = [ax[i] for i in indices]
fig.canvas.draw()
# Plot the lines
for a, l in zip(axes, lines):
# Transform the points into the figure coordinate system
transFigure = fig.transFigure.inverted()
endpoint0 = transFigure.transform(a.transData.transform(l[:, 0]))
endpoint1 = transFigure.transform(a.transData.transform(l[:, 1]))
fig.lines += [
matplotlib.lines.Line2D(
(endpoint0[i, 0], endpoint1[i, 0]),
(endpoint0[i, 1], endpoint1[i, 1]),
zorder=1,
transform=fig.transFigure,
c=colors[i],
alpha=alphas[i],
linewidth=lw,
)
for i in range(n_lines)
]
return fig
def make_matching_figure(
img0,
img1,
mkpts0,
mkpts1,
color,
titles=None,
kpts0=None,
kpts1=None,
text=[],
dpi=75,
path=None,
pad=0,
):
# 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)
if titles is not None:
axes[i].set_title(titles[i])
plt.tight_layout(pad=pad)
if kpts0 is not None:
assert kpts1 is not None
axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c="w", s=5)
axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c="w", s=5)
# 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=2,
)
for i in range(len(mkpts0))
]
# freeze the axes to prevent the transform to change
axes[0].autoscale(enable=False)
axes[1].autoscale(enable=False)
axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color[..., :3], s=4)
axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color[..., :3], 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 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,
)
np.random.seed(1995)
color_map = np.arange(100)
np.random.shuffle(color_map)
def fig2im(fig):
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
buf_ndarray = np.frombuffer(fig.canvas.tostring_rgb(), dtype="u1")
im = buf_ndarray.reshape(h, w, 3)
return im
def draw_matches(
mkpts0, mkpts1, img0, img1, conf, titles=None, dpi=150, path=None, pad=0.5
):
thr = 5e-4
thr = 0.5
color = error_colormap(conf, thr, alpha=0.1)
text = [
f"image name",
f"#Matches: {len(mkpts0)}",
]
if path:
fig2im(
make_matching_figure(
img0,
img1,
mkpts0,
mkpts1,
color,
titles=titles,
text=text,
path=path,
dpi=dpi,
pad=pad,
)
)
else:
return fig2im(
make_matching_figure(
img0,
img1,
mkpts0,
mkpts1,
color,
titles=titles,
text=text,
pad=pad,
dpi=dpi,
)
)
def draw_image_pairs(img0, img1, text=[], dpi=75, path=None, pad=0.5):
# draw image pair
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=pad)
# 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 fig2im(fig)
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