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import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def plot_images(imgs, titles=None, cmaps="gray", dpi=100, pad=0.5, adaptive=True):
"""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.
adaptive: whether the figure size should fit the image aspect ratios.
"""
n = len(imgs)
if not isinstance(cmaps, (list, tuple)):
cmaps = [cmaps] * n
if adaptive:
ratios = [i.shape[1] / i.shape[0] for i in imgs] # W / H
else:
ratios = [4 / 3] * n
figsize = [sum(ratios) * 4.5, 4.5]
fig, ax = plt.subplots(
1, n, figsize=figsize, dpi=dpi, gridspec_kw={"width_ratios": ratios}
)
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 ax
def plot_keypoints(kpts, colors="lime", ps=4, alpha=1):
"""Plot keypoints for existing images.
Args:
kpts: list of ndarrays of size (N, 2).
colors: string, or list of list of tuples (one for each keypoints).
ps: size of the keypoints as float.
"""
if not isinstance(colors, list):
colors = [colors] * len(kpts)
axes = plt.gcf().axes
for a, k, c in zip(axes, kpts, colors):
a.scatter(k[:, 0], k[:, 1], c=c, s=ps, alpha=alpha, linewidths=0)
def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.0):
"""Plot matches for a pair of existing images.
Args:
kpts0, kpts1: corresponding keypoints of size (N, 2).
color: color of each match, string or RGB tuple. Random if not given.
lw: width of the lines.
ps: size of the end points (no endpoint if ps=0)
indices: indices of the images to draw the matches on.
a: alpha opacity of the match lines.
"""
fig = plt.gcf()
ax = fig.axes
assert len(ax) > max(indices)
ax0, ax1 = ax[indices[0]], ax[indices[1]]
fig.canvas.draw()
assert len(kpts0) == len(kpts1)
if color is None:
color = matplotlib.cm.hsv(np.random.rand(len(kpts0))).tolist()
elif len(color) > 0 and not isinstance(color[0], (tuple, list)):
color = [color] * len(kpts0)
if lw > 0:
# transform the points into the figure coordinate system
transFigure = fig.transFigure.inverted()
fkpts0 = transFigure.transform(ax0.transData.transform(kpts0))
fkpts1 = transFigure.transform(ax1.transData.transform(kpts1))
fig.lines += [
matplotlib.lines.Line2D(
(fkpts0[i, 0], fkpts1[i, 0]),
(fkpts0[i, 1], fkpts1[i, 1]),
zorder=1,
transform=fig.transFigure,
c=color[i],
linewidth=lw,
alpha=a,
)
for i in range(len(kpts0))
]
# freeze the axes to prevent the transform to change
ax0.autoscale(enable=False)
ax1.autoscale(enable=False)
if ps > 0:
ax0.scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps)
ax1.scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps)
def plot_lines(
lines,
line_colors="orange",
point_colors="cyan",
ps=4,
lw=2,
alpha=1.0,
indices=(0, 1),
):
"""Plot lines and endpoints for existing images.
Args:
lines: list of ndarrays of size (N, 2, 2).
colors: string, or list of list of tuples (one for each keypoints).
ps: size of the keypoints as float pixels.
lw: line width as float pixels.
alpha: transparency of the points and lines.
indices: indices of the images to draw the matches on.
"""
if not isinstance(line_colors, list):
line_colors = [line_colors] * len(lines)
if not isinstance(point_colors, list):
point_colors = [point_colors] * len(lines)
fig = plt.gcf()
ax = fig.axes
assert len(ax) > max(indices)
axes = [ax[i] for i in indices]
fig.canvas.draw()
# Plot the lines and junctions
for a, l, lc, pc in zip(axes, lines, line_colors, point_colors):
for i in range(len(l)):
line = matplotlib.lines.Line2D(
(l[i, 0, 0], l[i, 1, 0]),
(l[i, 0, 1], l[i, 1, 1]),
zorder=1,
c=lc,
linewidth=lw,
alpha=alpha,
)
a.add_line(line)
pts = l.reshape(-1, 2)
a.scatter(pts[:, 0], pts[:, 1], c=pc, s=ps, linewidths=0, zorder=2, alpha=alpha)
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)
]
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