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# Copyright (c) Meta Platforms, Inc. and affiliates.
import numpy as np
import torch
import matplotlib.pyplot as plt
from utils.io import write_torch_image
from utils.viz_2d import plot_images, features_to_RGB, save_plot
from utils.viz_localization import (
likelihood_overlay,
plot_pose,
plot_dense_rotations,
add_circle_inset,
)
from osm.viz import Colormap, plot_nodes
def plot_example_single(
idx,
model,
pred,
data,
results,
plot_bev=True,
out_dir=None,
fig_for_paper=False,
show_gps=False,
show_fused=False,
show_dir_error=False,
show_masked_prob=False,
):
scene, name, rasters, uv_gt = (data[k] for k in ("scene", "name", "map", "uv"))
uv_gps = data.get("uv_gps")
yaw_gt = data["roll_pitch_yaw"][-1].numpy()
image = data["image"].permute(1, 2, 0)
if "valid" in data:
image = image.masked_fill(~data["valid"].unsqueeze(-1), 0.3)
lp_uvt = lp_uv = pred["log_probs"]
if show_fused and "log_probs_fused" in pred:
lp_uvt = lp_uv = pred["log_probs_fused"]
elif not show_masked_prob and "scores_unmasked" in pred:
lp_uvt = lp_uv = pred["scores_unmasked"]
has_rotation = lp_uvt.ndim == 3
if has_rotation:
lp_uv = lp_uvt.max(-1).values
if lp_uv.min() > -np.inf:
lp_uv = lp_uv.clip(min=np.percentile(lp_uv, 1))
prob = lp_uv.exp()
uv_p, yaw_p = pred["uv_max"], pred.get("yaw_max")
if show_fused and "uv_fused" in pred:
uv_p, yaw_p = pred["uv_fused"], pred.get("yaw_fused")
feats_map = pred["map"]["map_features"][0]
(feats_map_rgb,) = features_to_RGB(feats_map.numpy())
text1 = rf'$\Delta xy$: {results["xy_max_error"]:.1f}m'
if has_rotation:
text1 += rf', $\Delta\theta$: {results["yaw_max_error"]:.1f}°'
if show_fused and "xy_fused_error" in results:
text1 += rf', $\Delta xy_{{fused}}$: {results["xy_fused_error"]:.1f}m'
text1 += rf', $\Delta\theta_{{fused}}$: {results["yaw_fused_error"]:.1f}°'
if show_dir_error and "directional_error" in results:
err_lat, err_lon = results["directional_error"]
text1 += rf", $\Delta$lateral/longitundinal={err_lat:.1f}m/{err_lon:.1f}m"
if "xy_gps_error" in results:
text1 += rf', $\Delta xy_{{GPS}}$: {results["xy_gps_error"]:.1f}m'
map_viz = Colormap.apply(rasters)
overlay = likelihood_overlay(prob.numpy(), map_viz.mean(-1, keepdims=True))
plot_images(
[image, map_viz, overlay, feats_map_rgb],
titles=[text1, "map", "likelihood", "neural map"],
dpi=75,
cmaps="jet",
)
fig = plt.gcf()
axes = fig.axes
axes[1].images[0].set_interpolation("none")
axes[2].images[0].set_interpolation("none")
Colormap.add_colorbar()
plot_nodes(1, rasters[2])
if show_gps and uv_gps is not None:
plot_pose([1], uv_gps, c="blue")
plot_pose([1], uv_gt, yaw_gt, c="red")
plot_pose([1], uv_p, yaw_p, c="k")
plot_dense_rotations(2, lp_uvt.exp())
inset_center = pred["uv_max"] if results["xy_max_error"] < 5 else uv_gt
axins = add_circle_inset(axes[2], inset_center)
axins.scatter(*uv_gt, lw=1, c="red", ec="k", s=50, zorder=15)
axes[0].text(
0.003,
0.003,
f"{scene}/{name}",
transform=axes[0].transAxes,
fontsize=3,
va="bottom",
ha="left",
color="w",
)
plt.show()
if out_dir is not None:
name_ = name.replace("/", "_")
p = str(out_dir / f"{scene}_{name_}_{{}}.pdf")
save_plot(p.format("pred"))
plt.close()
if fig_for_paper:
# !cp ../datasets/MGL/{scene}/images/{name}.jpg {out_dir}/{scene}_{name}.jpg
plot_images([map_viz])
plt.gca().images[0].set_interpolation("none")
plot_nodes(0, rasters[2])
plot_pose([0], uv_gt, yaw_gt, c="red")
plot_pose([0], pred["uv_max"], pred["yaw_max"], c="k")
save_plot(p.format("map"))
plt.close()
plot_images([lp_uv], cmaps="jet")
plot_dense_rotations(0, lp_uvt.exp())
save_plot(p.format("loglikelihood"), dpi=100)
plt.close()
plot_images([overlay])
plt.gca().images[0].set_interpolation("none")
axins = add_circle_inset(plt.gca(), inset_center)
axins.scatter(*uv_gt, lw=1, c="red", ec="k", s=50)
save_plot(p.format("likelihood"))
plt.close()
write_torch_image(
p.format("neuralmap").replace("pdf", "jpg"), feats_map_rgb
)
write_torch_image(p.format("image").replace("pdf", "jpg"), image.numpy())
if not plot_bev:
return
feats_q = pred["features_bev"]
mask_bev = pred["valid_bev"]
prior = None
if "log_prior" in pred["map"]:
prior = pred["map"]["log_prior"][0].sigmoid()
if "bev" in pred and "confidence" in pred["bev"]:
conf_q = pred["bev"]["confidence"]
else:
conf_q = torch.norm(feats_q, dim=0)
conf_q = conf_q.masked_fill(~mask_bev, np.nan)
(feats_q_rgb,) = features_to_RGB(feats_q.numpy(), masks=[mask_bev.numpy()])
# feats_map_rgb, feats_q_rgb, = features_to_RGB(
# feats_map.numpy(), feats_q.numpy(), masks=[None, mask_bev])
norm_map = torch.norm(feats_map, dim=0)
plot_images(
[conf_q, feats_q_rgb, norm_map] + ([] if prior is None else [prior]),
titles=["BEV confidence", "BEV features", "map norm"]
+ ([] if prior is None else ["map prior"]),
dpi=50,
cmaps="jet",
)
plt.show()
if out_dir is not None:
save_plot(p.format("bev"))
plt.close()
def plot_example_sequential(
idx,
model,
pred,
data,
results,
plot_bev=True,
out_dir=None,
fig_for_paper=False,
show_gps=False,
show_fused=False,
show_dir_error=False,
show_masked_prob=False,
):
return
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