286 lines
No EOL
12 KiB
Python
286 lines
No EOL
12 KiB
Python
import os
|
|
import json
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
|
|
import matplotlib.patheffects as pe
|
|
from scipy.ndimage import rotate
|
|
import seaborn as sns
|
|
|
|
from model.model_registrar import ModelRegistrar
|
|
from model import Trajectron
|
|
from utils import prediction_output_to_trajectories
|
|
|
|
from scipy.integrate import cumtrapz
|
|
|
|
line_colors = ['#375397', '#F05F78', '#80CBE5', '#ABCB51', '#C8B0B0']
|
|
|
|
cars = [plt.imread('icons/Car TOP_VIEW 375397.png'),
|
|
plt.imread('icons/Car TOP_VIEW F05F78.png'),
|
|
plt.imread('icons/Car TOP_VIEW 80CBE5.png'),
|
|
plt.imread('icons/Car TOP_VIEW ABCB51.png'),
|
|
plt.imread('icons/Car TOP_VIEW C8B0B0.png')]
|
|
|
|
robot = plt.imread('icons/Car TOP_VIEW ROBOT.png')
|
|
|
|
|
|
def load_model(model_dir, env, ts=3999):
|
|
model_registrar = ModelRegistrar(model_dir, 'cpu')
|
|
model_registrar.load_models(ts)
|
|
with open(os.path.join(model_dir, 'config.json'), 'r') as config_json:
|
|
hyperparams = json.load(config_json)
|
|
|
|
hyperparams['map_enc_dropout'] = 0.0
|
|
if 'incl_robot_node' not in hyperparams:
|
|
hyperparams['incl_robot_node'] = False
|
|
|
|
stg = Trajectron(model_registrar, hyperparams, None, 'cpu')
|
|
|
|
stg.set_environment(env)
|
|
|
|
stg.set_annealing_params()
|
|
|
|
return stg, hyperparams
|
|
|
|
|
|
def plot_vehicle_nice(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0, y_min=0):
|
|
prediction_dict, histories_dict, futures_dict = prediction_output_to_trajectories(predictions,
|
|
dt,
|
|
max_hl,
|
|
ph,
|
|
map=map)
|
|
assert (len(prediction_dict.keys()) <= 1)
|
|
if len(prediction_dict.keys()) == 0:
|
|
return
|
|
ts_key = list(prediction_dict.keys())[0]
|
|
|
|
prediction_dict = prediction_dict[ts_key]
|
|
histories_dict = histories_dict[ts_key]
|
|
futures_dict = futures_dict[ts_key]
|
|
|
|
if map is not None:
|
|
ax.imshow(map.fdata, origin='lower', alpha=0.5)
|
|
|
|
cmap = ['k', 'b', 'y', 'g', 'r']
|
|
line_alpha = 0.7
|
|
line_width = 0.2
|
|
edge_width = 2
|
|
circle_edge_width = 0.5
|
|
node_circle_size = 0.3
|
|
a = []
|
|
i = 0
|
|
node_list = sorted(histories_dict.keys(), key=lambda x: x.id)
|
|
for node in node_list:
|
|
history = histories_dict[node] + np.array([x_min, y_min])
|
|
future = futures_dict[node] + np.array([x_min, y_min])
|
|
predictions = prediction_dict[node] + np.array([x_min, y_min])
|
|
if node.type.name == 'VEHICLE':
|
|
# ax.plot(history[:, 0], history[:, 1], 'ko-', linewidth=1)
|
|
|
|
ax.plot(future[:, 0],
|
|
future[:, 1],
|
|
'w--o',
|
|
linewidth=4,
|
|
markersize=3,
|
|
zorder=650,
|
|
path_effects=[pe.Stroke(linewidth=5, foreground='k'), pe.Normal()])
|
|
|
|
for t in range(predictions.shape[2]):
|
|
sns.kdeplot(predictions[0, :, t, 0], predictions[0, :, t, 1],
|
|
ax=ax, shade=True, shade_lowest=False,
|
|
color=line_colors[i % len(line_colors)], zorder=600, alpha=0.8)
|
|
|
|
vel = node.get(np.array([ts_key]), {'velocity': ['x', 'y']})
|
|
h = np.arctan2(vel[0, 1], vel[0, 0])
|
|
r_img = rotate(cars[i % len(cars)], node.get(np.array([ts_key]), {'heading': ['°']})[0, 0] * 180 / np.pi,
|
|
reshape=True)
|
|
oi = OffsetImage(r_img, zoom=0.025, zorder=700)
|
|
veh_box = AnnotationBbox(oi, (history[-1, 0], history[-1, 1]), frameon=False)
|
|
veh_box.zorder = 700
|
|
ax.add_artist(veh_box)
|
|
i += 1
|
|
else:
|
|
# ax.plot(history[:, 0], history[:, 1], 'k--')
|
|
|
|
for t in range(predictions.shape[2]):
|
|
sns.kdeplot(predictions[0, :, t, 0], predictions[0, :, t, 1],
|
|
ax=ax, shade=True, shade_lowest=False,
|
|
color='b', zorder=600, alpha=0.8)
|
|
|
|
ax.plot(future[:, 0],
|
|
future[:, 1],
|
|
'w--',
|
|
zorder=650,
|
|
path_effects=[pe.Stroke(linewidth=edge_width, foreground='k'), pe.Normal()])
|
|
# Current Node Position
|
|
circle = plt.Circle((history[-1, 0],
|
|
history[-1, 1]),
|
|
node_circle_size,
|
|
facecolor='g',
|
|
edgecolor='k',
|
|
lw=circle_edge_width,
|
|
zorder=3)
|
|
ax.add_artist(circle)
|
|
|
|
|
|
def plot_vehicle_mm(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0, y_min=0):
|
|
prediction_dict, histories_dict, futures_dict = prediction_output_to_trajectories(predictions,
|
|
dt,
|
|
max_hl,
|
|
ph,
|
|
map=map)
|
|
assert (len(prediction_dict.keys()) <= 1)
|
|
if len(prediction_dict.keys()) == 0:
|
|
return
|
|
ts_key = list(prediction_dict.keys())[0]
|
|
|
|
prediction_dict = prediction_dict[ts_key]
|
|
histories_dict = histories_dict[ts_key]
|
|
futures_dict = futures_dict[ts_key]
|
|
|
|
if map is not None:
|
|
ax.imshow(map.fdata, origin='lower', alpha=0.5)
|
|
|
|
cmap = ['k', 'b', 'y', 'g', 'r']
|
|
line_alpha = 0.7
|
|
line_width = 0.2
|
|
edge_width = 2
|
|
circle_edge_width = 0.5
|
|
node_circle_size = 0.5
|
|
a = []
|
|
i = 0
|
|
node_list = sorted(histories_dict.keys(), key=lambda x: x.id)
|
|
for node in node_list:
|
|
history = histories_dict[node] + np.array([x_min, y_min])
|
|
future = futures_dict[node] + np.array([x_min, y_min])
|
|
|
|
predictions = prediction_dict[node] + np.array([x_min, y_min])
|
|
if node.type.name == 'VEHICLE':
|
|
for sample_num in range(prediction_dict[node].shape[1]):
|
|
ax.plot(predictions[:, sample_num, :, 0], predictions[:, sample_num, :, 1], 'ko-',
|
|
zorder=620,
|
|
markersize=5,
|
|
linewidth=3, alpha=0.7)
|
|
else:
|
|
for sample_num in range(prediction_dict[node].shape[1]):
|
|
ax.plot(predictions[:, sample_num, :, 0], predictions[:, sample_num, :, 1], 'ko-',
|
|
zorder=620,
|
|
markersize=2,
|
|
linewidth=1, alpha=0.7)
|
|
|
|
|
|
def plot_vehicle_nice_mv(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0, y_min=0):
|
|
prediction_dict, histories_dict, futures_dict = prediction_output_to_trajectories(predictions,
|
|
dt,
|
|
max_hl,
|
|
ph,
|
|
map=map)
|
|
assert (len(prediction_dict.keys()) <= 1)
|
|
if len(prediction_dict.keys()) == 0:
|
|
return
|
|
ts_key = list(prediction_dict.keys())[0]
|
|
|
|
prediction_dict = prediction_dict[ts_key]
|
|
histories_dict = histories_dict[ts_key]
|
|
futures_dict = futures_dict[ts_key]
|
|
|
|
if map is not None:
|
|
ax.imshow(map.fdata, origin='lower', alpha=0.5)
|
|
|
|
cmap = ['k', 'b', 'y', 'g', 'r']
|
|
line_alpha = 0.7
|
|
line_width = 0.2
|
|
edge_width = 2
|
|
circle_edge_width = 0.5
|
|
node_circle_size = 0.3
|
|
a = []
|
|
i = 0
|
|
node_list = sorted(histories_dict.keys(), key=lambda x: x.id)
|
|
for node in node_list:
|
|
h = node.get(np.array([ts_key]), {'heading': ['°']})[0, 0]
|
|
history_org = histories_dict[node] + np.array([x_min, y_min])
|
|
history = histories_dict[node] + np.array([x_min, y_min]) + 5 * np.array([np.cos(h), np.sin(h)])
|
|
future = futures_dict[node] + np.array([x_min, y_min]) + 5 * np.array([np.cos(h), np.sin(h)])
|
|
predictions = prediction_dict[node] + np.array([x_min, y_min]) + 5 * np.array([np.cos(h), np.sin(h)])
|
|
if node.type.name == 'VEHICLE':
|
|
for t in range(predictions.shape[2]):
|
|
sns.kdeplot(predictions[0, :, t, 0], predictions[0, :, t, 1],
|
|
ax=ax, shade=True, shade_lowest=False,
|
|
color=line_colors[i % len(line_colors)], zorder=600, alpha=1.0)
|
|
|
|
r_img = rotate(cars[i % len(cars)], node.get(np.array([ts_key]), {'heading': ['°']})[0, 0] * 180 / np.pi,
|
|
reshape=True)
|
|
oi = OffsetImage(r_img, zoom=0.08, zorder=700)
|
|
veh_box = AnnotationBbox(oi, (history_org[-1, 0], history_org[-1, 1]), frameon=False)
|
|
veh_box.zorder = 700
|
|
ax.add_artist(veh_box)
|
|
i += 1
|
|
else:
|
|
|
|
for t in range(predictions.shape[2]):
|
|
sns.kdeplot(predictions[:, t, 0], predictions[:, t, 1],
|
|
ax=ax, shade=True, shade_lowest=False,
|
|
color='b', zorder=600, alpha=0.8)
|
|
|
|
# Current Node Position
|
|
circle = plt.Circle((history[-1, 0],
|
|
history[-1, 1]),
|
|
node_circle_size,
|
|
facecolor='g',
|
|
edgecolor='k',
|
|
lw=circle_edge_width,
|
|
zorder=3)
|
|
ax.add_artist(circle)
|
|
|
|
|
|
def plot_vehicle_nice_mv_robot(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0, y_min=0):
|
|
prediction_dict, histories_dict, futures_dict = prediction_output_to_trajectories(predictions,
|
|
dt,
|
|
max_hl,
|
|
ph,
|
|
map=map)
|
|
assert (len(prediction_dict.keys()) <= 1)
|
|
if len(prediction_dict.keys()) == 0:
|
|
return
|
|
ts_key = list(prediction_dict.keys())[0]
|
|
|
|
prediction_dict = prediction_dict[ts_key]
|
|
histories_dict = histories_dict[ts_key]
|
|
futures_dict = futures_dict[ts_key]
|
|
|
|
if map is not None:
|
|
ax.imshow(map.fdata, origin='lower', alpha=0.5)
|
|
|
|
cmap = ['k', 'b', 'y', 'g', 'r']
|
|
line_alpha = 0.7
|
|
line_width = 0.2
|
|
edge_width = 2
|
|
circle_edge_width = 0.5
|
|
node_circle_size = 0.3
|
|
|
|
node_list = sorted(histories_dict.keys(), key=lambda x: x.id)
|
|
for node in node_list:
|
|
h = node.get(np.array([ts_key]), {'heading': ['°']})[0, 0]
|
|
history_org = histories_dict[node] + np.array([x_min, y_min]) + 5 / 2 * np.array(
|
|
[np.cos(h), np.sin(h)])
|
|
future = futures_dict[node] + np.array([x_min, y_min]) + 5 * np.array([np.cos(h), np.sin(h)])
|
|
|
|
ax.plot(future[:, 0],
|
|
future[:, 1],
|
|
'--o',
|
|
c='#F05F78',
|
|
linewidth=4,
|
|
markersize=3,
|
|
zorder=650,
|
|
path_effects=[pe.Stroke(linewidth=5, foreground='k'), pe.Normal()])
|
|
|
|
r_img = rotate(robot, node.get(np.array([ts_key]), {'heading': ['°']})[0, 0] * 180 / np.pi, reshape=True)
|
|
oi = OffsetImage(r_img, zoom=0.08, zorder=700)
|
|
veh_box = AnnotationBbox(oi, (history_org[-1, 0], history_org[-1, 1]), frameon=False)
|
|
veh_box.zorder = 700
|
|
ax.add_artist(veh_box)
|
|
|
|
|
|
def integrate(f, dx, F0=0.):
|
|
N = f.shape[0]
|
|
return F0 + np.hstack((np.zeros((N, 1)), cumtrapz(f, axis=1, dx=dx))) |