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import bisect
import numpy as np
import matplotlib.pyplot as plt
import matplotlib, os, cv2
import matplotlib.cm as cm
from PIL import Image
import torch.nn.functional as F
import torch
import seaborn as sns
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
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 _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}%):"
f" {n_correct}/{len(kpts0)}",
f"Recall({conf_thr:.2e}) ({100 * recall:.1f}%):"
f" {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,
)
np.random.seed(1995)
color_map = np.arange(100)
np.random.shuffle(color_map)
def draw_topics(
data,
img0,
img1,
saved_folder="viz_topics",
show_n_topics=8,
saved_name=None,
):
topic0, topic1 = data["topic_matrix"]["img0"], data["topic_matrix"]["img1"]
hw0_c, hw1_c = data["hw0_c"], data["hw1_c"]
hw0_i, hw1_i = data["hw0_i"], data["hw1_i"]
# print(hw0_i, hw1_i)
scale0, scale1 = hw0_i[0] // hw0_c[0], hw1_i[0] // hw1_c[0]
if "scale0" in data:
scale0 *= data["scale0"][0]
else:
scale0 = (scale0, scale0)
if "scale1" in data:
scale1 *= data["scale1"][0]
else:
scale1 = (scale1, scale1)
n_topics = topic0.shape[-1]
# mask0_nonzero = topic0[0].sum(dim=-1, keepdim=True) > 0
# mask1_nonzero = topic1[0].sum(dim=-1, keepdim=True) > 0
theta0 = topic0[0].sum(dim=0)
theta0 /= theta0.sum().float()
theta1 = topic1[0].sum(dim=0)
theta1 /= theta1.sum().float()
# top_topic0 = torch.argsort(theta0, descending=True)[:show_n_topics]
# top_topic1 = torch.argsort(theta1, descending=True)[:show_n_topics]
top_topics = torch.argsort(theta0 * theta1, descending=True)[:show_n_topics]
# print(sum_topic0, sum_topic1)
topic0 = topic0[0].argmax(
dim=-1, keepdim=True
) # .float() / (n_topics - 1) #* 255 + 1 #
# topic0[~mask0_nonzero] = -1
topic1 = topic1[0].argmax(
dim=-1, keepdim=True
) # .float() / (n_topics - 1) #* 255 + 1
# topic1[~mask1_nonzero] = -1
label_img0, label_img1 = (
torch.zeros_like(topic0) - 1,
torch.zeros_like(topic1) - 1,
)
for i, k in enumerate(top_topics):
label_img0[topic0 == k] = color_map[k]
label_img1[topic1 == k] = color_map[k]
# print(hw0_c, scale0)
# print(hw1_c, scale1)
# map_topic0 = F.fold(label_img0.unsqueeze(0), hw0_i, kernel_size=scale0, stride=scale0)
map_topic0 = (
label_img0.float().view(hw0_c).cpu().numpy()
) # map_topic0.squeeze(0).squeeze(0).cpu().numpy()
map_topic0 = cv2.resize(
map_topic0, (int(hw0_c[1] * scale0[0]), int(hw0_c[0] * scale0[1]))
)
# map_topic1 = F.fold(label_img1.unsqueeze(0), hw1_i, kernel_size=scale1, stride=scale1)
map_topic1 = (
label_img1.float().view(hw1_c).cpu().numpy()
) # map_topic1.squeeze(0).squeeze(0).cpu().numpy()
map_topic1 = cv2.resize(
map_topic1, (int(hw1_c[1] * scale1[0]), int(hw1_c[0] * scale1[1]))
)
# show image0
if saved_name is None:
return map_topic0, map_topic1
if not os.path.exists(saved_folder):
os.makedirs(saved_folder)
path_saved_img0 = os.path.join(saved_folder, "{}_0.png".format(saved_name))
plt.imshow(img0)
masked_map_topic0 = np.ma.masked_where(map_topic0 < 0, map_topic0)
plt.imshow(
masked_map_topic0,
cmap=plt.cm.jet,
vmin=0,
vmax=n_topics - 1,
alpha=0.3,
interpolation="bilinear",
)
# plt.show()
plt.axis("off")
plt.savefig(path_saved_img0, bbox_inches="tight", pad_inches=0, dpi=250)
plt.close()
path_saved_img1 = os.path.join(saved_folder, "{}_1.png".format(saved_name))
plt.imshow(img1)
masked_map_topic1 = np.ma.masked_where(map_topic1 < 0, map_topic1)
plt.imshow(
masked_map_topic1,
cmap=plt.cm.jet,
vmin=0,
vmax=n_topics - 1,
alpha=0.3,
interpolation="bilinear",
)
plt.axis("off")
plt.savefig(path_saved_img1, bbox_inches="tight", pad_inches=0, dpi=250)
plt.close()
def draw_topicfm_demo(
data,
img0,
img1,
mkpts0,
mkpts1,
mcolor,
text,
show_n_topics=8,
topic_alpha=0.3,
margin=5,
path=None,
opencv_display=False,
opencv_title="",
):
topic_map0, topic_map1 = draw_topics(
data, img0, img1, show_n_topics=show_n_topics
)
mask_tm0, mask_tm1 = np.expand_dims(
topic_map0 >= 0, axis=-1
), np.expand_dims(topic_map1 >= 0, axis=-1)
topic_cm0, topic_cm1 = cm.jet(topic_map0 / 99.0), cm.jet(topic_map1 / 99.0)
topic_cm0 = cv2.cvtColor(
topic_cm0[..., :3].astype(np.float32), cv2.COLOR_RGB2BGR
)
topic_cm1 = cv2.cvtColor(
topic_cm1[..., :3].astype(np.float32), cv2.COLOR_RGB2BGR
)
overlay0 = (mask_tm0 * topic_cm0 + (1 - mask_tm0) * img0).astype(np.float32)
overlay1 = (mask_tm1 * topic_cm1 + (1 - mask_tm1) * img1).astype(np.float32)
cv2.addWeighted(overlay0, topic_alpha, img0, 1 - topic_alpha, 0, overlay0)
cv2.addWeighted(overlay1, topic_alpha, img1, 1 - topic_alpha, 0, overlay1)
overlay0, overlay1 = (overlay0 * 255).astype(np.uint8), (
overlay1 * 255
).astype(np.uint8)
h0, w0 = img0.shape[:2]
h1, w1 = img1.shape[:2]
h, w = h0 * 2 + margin * 2, w0 * 2 + margin
out_fig = 255 * np.ones((h, w, 3), dtype=np.uint8)
out_fig[:h0, :w0] = overlay0
if h0 >= h1:
start = (h0 - h1) // 2
out_fig[
start : (start + h1), (w0 + margin) : (w0 + margin + w1)
] = overlay1
else:
start = (h1 - h0) // 2
out_fig[:h0, (w0 + margin) : (w0 + margin + w1)] = overlay1[
start : (start + h0)
]
step_h = h0 + margin * 2
out_fig[step_h : step_h + h0, :w0] = (img0 * 255).astype(np.uint8)
if h0 >= h1:
start = step_h + (h0 - h1) // 2
out_fig[start : start + h1, (w0 + margin) : (w0 + margin + w1)] = (
img1 * 255
).astype(np.uint8)
else:
start = (h1 - h0) // 2
out_fig[step_h : step_h + h0, (w0 + margin) : (w0 + margin + w1)] = (
img1[start : start + h0] * 255
).astype(np.uint8)
# draw matching lines, this is inspried from
# https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/utils.py
mkpts0, mkpts1 = np.round(mkpts0).astype(int), np.round(mkpts1).astype(int)
mcolor = (np.array(mcolor[:, [2, 1, 0]]) * 255).astype(int)
for (x0, y0), (x1, y1), c in zip(mkpts0, mkpts1, mcolor):
c = c.tolist()
cv2.line(
out_fig,
(x0, y0 + step_h),
(x1 + margin + w0, y1 + step_h + (h0 - h1) // 2),
color=c,
thickness=1,
lineType=cv2.LINE_AA,
)
# display line end-points as circles
cv2.circle(out_fig, (x0, y0 + step_h), 2, c, -1, lineType=cv2.LINE_AA)
cv2.circle(
out_fig,
(x1 + margin + w0, y1 + step_h + (h0 - h1) // 2),
2,
c,
-1,
lineType=cv2.LINE_AA,
)
# Scale factor for consistent visualization across scales.
sc = min(h / 960.0, 2.0)
# Big text.
Ht = int(30 * sc) # text height
txt_color_fg = (255, 255, 255)
txt_color_bg = (0, 0, 0)
for i, t in enumerate(text):
cv2.putText(
out_fig,
t,
(int(8 * sc), Ht + step_h * i),
cv2.FONT_HERSHEY_DUPLEX,
1.0 * sc,
txt_color_bg,
2,
cv2.LINE_AA,
)
cv2.putText(
out_fig,
t,
(int(8 * sc), Ht + step_h * i),
cv2.FONT_HERSHEY_DUPLEX,
1.0 * sc,
txt_color_fg,
1,
cv2.LINE_AA,
)
if path is not None:
cv2.imwrite(str(path), out_fig)
if opencv_display:
cv2.imshow(opencv_title, out_fig)
cv2.waitKey(1)
return out_fig
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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