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"""
2D visualization primitives based on Matplotlib.
1) Plot images with `plot_images`.
2) Call `plot_keypoints` or `plot_matches` any number of times.
3) Optionally: save a .png or .pdf plot (nice in papers!) with `save_plot`.
"""
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
import numpy as np
import torch
def cm_RdGn(x):
"""Custom colormap: red (0) -> yellow (0.5) -> green (1)."""
x = np.clip(x, 0, 1)[..., None]*2
c = x*np.array([[0, 1., 0]]) + (2-x)*np.array([[1., 0, 0]])
return np.clip(c, 0, 1)
def cm_BlRdGn(x_):
"""Custom colormap: blue (-1) -> red (0.0) -> green (1)."""
x = np.clip(x_, 0, 1)[..., None]*2
c = x*np.array([[0, 1., 0, 1.]]) + (2-x)*np.array([[1., 0, 0, 1.]])
xn = -np.clip(x_, -1, 0)[..., None]*2
cn = xn*np.array([[0, 0.1, 1, 1.]]) + (2-xn)*np.array([[1., 0, 0, 1.]])
out = np.clip(np.where(x_[..., None] < 0, cn, c), 0, 1)
return out
def cm_prune(x_):
""" Custom colormap to visualize pruning """
if isinstance(x_, torch.Tensor):
x_ = x_.cpu().numpy()
max_i = max(x_)
norm_x = np.where(x_ == max_i, -1, (x_-1) / 9)
return cm_BlRdGn(norm_x)
def plot_images(imgs, titles=None, cmaps='gray', dpi=100, pad=.5,
adaptive=True):
"""Plot a set of images horizontally.
Args:
imgs: list of NumPy RGB (H, W, 3) or PyTorch RGB (3, H, W) 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.
"""
# conversion to (H, W, 3) for torch.Tensor
imgs = [img.permute(1, 2, 0).cpu().numpy()
if (isinstance(img, torch.Tensor) and img.dim() == 3) else img
for img in imgs]
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)
def plot_keypoints(kpts, colors='lime', ps=4, axes=None, a=1.0):
"""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)
if not isinstance(a, list):
a = [a] * len(kpts)
if axes is None:
axes = plt.gcf().axes
for ax, k, c, alpha in zip(axes, kpts, colors, a):
if isinstance(k, torch.Tensor):
k = k.cpu().numpy()
ax.scatter(k[:, 0], k[:, 1], c=c, s=ps, linewidths=0, alpha=alpha)
def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, a=1., labels=None,
axes=None):
"""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()
if axes is None:
ax = fig.axes
ax0, ax1 = ax[0], ax[1]
else:
ax0, ax1 = axes
if isinstance(kpts0, torch.Tensor):
kpts0 = kpts0.cpu().numpy()
if isinstance(kpts1, torch.Tensor):
kpts1 = kpts1.cpu().numpy()
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:
for i in range(len(kpts0)):
line = matplotlib.patches.ConnectionPatch(
xyA=(kpts0[i, 0], kpts0[i, 1]), xyB=(kpts1[i, 0], kpts1[i, 1]),
coordsA=ax0.transData, coordsB=ax1.transData,
axesA=ax0, axesB=ax1,
zorder=1, color=color[i], linewidth=lw, clip_on=True,
alpha=a, label=None if labels is None else labels[i],
picker=5.0)
line.set_annotation_clip(True)
fig.add_artist(line)
# 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 add_text(idx, text, pos=(0.01, 0.99), fs=15, color='w',
lcolor='k', lwidth=2, ha='left', va='top'):
ax = plt.gcf().axes[idx]
t = ax.text(*pos, text, fontsize=fs, ha=ha, va=va,
color=color, transform=ax.transAxes)
if lcolor is not None:
t.set_path_effects([
path_effects.Stroke(linewidth=lwidth, foreground=lcolor),
path_effects.Normal()])
def save_plot(path, **kw):
"""Save the current figure without any white margin."""
plt.savefig(path, bbox_inches='tight', pad_inches=0, **kw)
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