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from typing import Optional
import matplotlib
import matplotlib.pylab as plt
import numpy as np
from PIL import Image
import os
import torch
import matplotlib
from typing import Tuple, Optional, List, Dict, Any, Union
from matplotlib.patches import Circle, Polygon, RegularPolygon
from gpudrive.visualize.color import ROAD_GRAPH_COLORS, ROAD_GRAPH_TYPE_NAMES
def img_from_fig(fig: matplotlib.figure.Figure) -> np.ndarray:
"""Returns a [H, W, 3] uint8 np image from fig.canvas.tostring_rgb()."""
# Adjusted margins to better accommodate 3D plots
fig.subplots_adjust(
left=0.0, # Reduce left margin
bottom=0.0, # Reduce bottom margin
right=1.0, # Extend to right edge
top=1.0, # Extend to top edge
wspace=0.0,
hspace=0.0
)
# Force render
fig.canvas.draw()
# Convert to numpy array
data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
img = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close(fig)
return img
def save_img_as_png(img: np.ndarray, filename: str = "/tmp/img.png"):
"""Saves np image to disk."""
outdir = os.path.dirname(filename)
os.makedirs(outdir, exist_ok=True)
Image.fromarray(img).save(filename)
def plot_roadgraph_points(ax, observation_roadgraph, env_idx, agent_idx):
"""Plots the road graph points by their type, using names instead of type numbers."""
# Extract road graph types and positions
roadgraph_types = observation_roadgraph.type[env_idx, agent_idx, :]
roadgraph_x = observation_roadgraph.x[env_idx, agent_idx, :]
roadgraph_y = observation_roadgraph.y[env_idx, agent_idx, :]
# Plot points by type, mapping types to names
for road_type, color in ROAD_GRAPH_COLORS.items():
# Filter points by road type
idx = roadgraph_types == road_type
if idx.sum() > 0:
ax.plot(
roadgraph_x[idx],
roadgraph_y[idx],
".", # Plot as dots
color=color,
label=ROAD_GRAPH_TYPE_NAMES.get(
road_type, f"Type {road_type}"
),
)
def plot_numpy_bounding_boxes(
ax: matplotlib.axes.Axes,
bboxes: np.ndarray,
color: np.ndarray,
alpha: Optional[float] = 1.0,
line_width_scale: float = 1.5,
as_center_pts: bool = False,
label: Optional[str] = None,
) -> None:
"""Plots multiple bounding boxes.
Args:
ax: Fig handles.
bboxes: Shape (num_bbox, 5), with last dimension as (x, y, length, width,
yaw).
color: Shape (3,), represents RGB color for drawing.
alpha: Alpha value for drawing, i.e. 0 means fully transparent.
as_center_pts: If set to True, bboxes will be drawn as center points,
instead of full bboxes.
label: String, represents the meaning of the color for different boxes.
"""
if bboxes.ndim != 2 or bboxes.shape[1] != 5:
raise ValueError(
(
"Expect bboxes rank 2, last dimension of bbox 5"
" got{}, {}, {} respectively"
).format(bboxes.ndim, bboxes.shape[1], color.shape)
)
if as_center_pts:
ax.plot(
bboxes[:, 0],
bboxes[:, 1],
"o",
color=color,
ms=2,
alpha=alpha,
linewidth=1.7 * line_width_scale,
label=label,
)
else:
c = np.cos(bboxes[:, 4])
s = np.sin(bboxes[:, 4])
pt = np.array((bboxes[:, 0], bboxes[:, 1])) # (2, N)
length, width = bboxes[:, 2], bboxes[:, 3]
u = np.array((c, s))
ut = np.array((s, -c))
# Compute box corner coordinates.
tl = pt + length / 2 * u - width / 2 * ut
tr = pt + length / 2 * u + width / 2 * ut
br = pt - length / 2 * u + width / 2 * ut
bl = pt - length / 2 * u - width / 2 * ut
# Compute heading arrow using center left/right/front.
cl = pt - width / 2 * ut
cr = pt + width / 2 * ut
cf = pt + length / 2 * u
# Draw bboxes.
ax.plot(
[tl[0, :], tr[0, :], br[0, :], bl[0, :], tl[0, :]],
[tl[1, :], tr[1, :], br[1, :], bl[1, :], tl[1, :]],
color=color,
zorder=4,
linewidth=1.7 * line_width_scale,
alpha=alpha,
label=label,
)
# Draw heading arrow.
ax.plot(
[cl[0, :], cr[0, :], cf[0, :], cl[0, :]],
[cl[1, :], cr[1, :], cf[1, :], cl[1, :]],
color=color,
zorder=6,
alpha=alpha,
linewidth=1.5 * line_width_scale,
label=label,
)
def plot_bounding_box(
ax: matplotlib.axes.Axes,
center: Optional[Union[Tuple[float, float], torch.Tensor]],
vehicle_length: Union[float, torch.Tensor],
vehicle_width: Union[float, torch.Tensor],
orientation: Union[float, torch.Tensor],
color: str,
alpha: Optional[float] = 1.0,
label: Optional[str] = None,
) -> None:
"""Plots bounding boxes, supporting both single and multiple agents.
Args:
ax: Matplotlib Axes handle.
center: Tuple (x, y) specifying a single bounding box center or
a tensor of shape (num_agents, 2) with x, y positions for multiple agents.
vehicle_length: Length of the bounding box (float or tensor of shape (num_agents,)).
vehicle_width: Width of the bounding box (float or tensor of shape (num_agents,)).
orientation: Orientation of the bounding box (float or tensor of shape (num_agents,)).
color: Color for the bounding boxes.
alpha: Transparency of the bounding boxes (0.0 to 1.0).
label: Optional label for the bounding boxes (only used for single-agent plots).
"""
if isinstance(center, torch.Tensor):
# Multiple bounding boxes
if center.shape[-1] != 2:
raise ValueError(
"Center tensor must have shape (num_agents, 2) for multiple bounding boxes."
)
num_agents = center.shape[0]
for i in range(num_agents):
cx, cy = center[i]
length = vehicle_length[i].item()
width = vehicle_width[i].item()
angle = orientation[i].item()
# Compute bounding box corners
corners_x = [
cx - length / 2,
cx + length / 2,
cx + length / 2,
cx - length / 2,
cx - length / 2,
]
corners_y = [
cy - width / 2,
cy - width / 2,
cy + width / 2,
cy + width / 2,
cy - width / 2,
]
# Apply rotation
rotated_corners = [
(
(x - cx) * np.cos(angle) - (y - cy) * np.sin(angle) + cx,
(x - cx) * np.sin(angle) + (y - cy) * np.cos(angle) + cy,
)
for x, y in zip(corners_x, corners_y)
]
rotated_corners_x, rotated_corners_y = zip(*rotated_corners)
ax.plot(
np.concatenate(
[rotated_corners_x]
), # Use np.concatenate to fix the addition
np.concatenate(
[rotated_corners_y]
), # Use np.concatenate to fix the addition
color=color,
alpha=alpha,
linestyle="-",
linewidth=2,
label=label if i == 0 else None,
)
else:
# Single bounding box
cx, cy = center
corners_x = [
cx - vehicle_length / 2,
cx + vehicle_length / 2,
cx + vehicle_length / 2,
cx - vehicle_length / 2,
cx - vehicle_length / 2,
]
corners_y = [
cy - vehicle_width / 2,
cy - vehicle_width / 2,
cy + vehicle_width / 2,
cy + vehicle_width / 2,
cy - vehicle_width / 2,
]
# Apply rotation for single bounding box
rotated_corners = [
(
(x - cx) * np.cos(orientation)
- (y - cy) * np.sin(orientation)
+ cx,
(x - cx) * np.sin(orientation)
+ (y - cy) * np.cos(orientation)
+ cy,
)
for x, y in zip(corners_x, corners_y)
]
rotated_corners_x, rotated_corners_y = zip(*rotated_corners)
ax.plot(
np.concatenate([rotated_corners_x]),
np.concatenate([rotated_corners_y]),
color=color,
alpha=alpha,
linestyle="-",
label=label,
linewidth=2,
)
def get_corners_polygon(x, y, length, width, orientation):
"""Calculate the four corners of a speed bump (can be any) polygon."""
# Compute the direction vectors based on orientation
# print(length)
c = np.cos(orientation)
s = np.sin(orientation)
u = np.array((c, s)) # Unit vector along the orientation
ut = np.array((-s, c)) # Unit vector perpendicular to the orientation
# Center point of the speed bump
pt = np.array([x, y])
# corners
tl = pt + (length / 2) * u - (width / 2) * ut
tr = pt + (length / 2) * u + (width / 2) * ut
br = pt - (length / 2) * u + (width / 2) * ut
bl = pt - (length / 2) * u - (width / 2) * ut
return [tl.tolist(), tr.tolist(), br.tolist(), bl.tolist()]
def get_stripe_polygon(
x: float,
y: float,
length: float,
width: float,
orientation: float,
index: int,
num_stripes: int,
) -> np.ndarray:
"""Calculate the corners of a stripe within the speed bump polygon."""
# Compute the direction vectors
c = np.cos(orientation)
s = np.sin(orientation)
u = np.array([c, s]) # Unit vector along the orientation (lengthwise)
ut = np.array([-s, c]) # Perpendicular unit vector (widthwise)
# Total stripe height along the width
stripe_width = length / num_stripes
half_length = length / 2
half_width = width / 2
# Offset for the current stripe
offset_start = -half_length + index * stripe_width
offset_end = offset_start + stripe_width
# Center of the speed bump
center = np.array([x, y])
# Calculate stripe corners
stripe_corners = [
center + u * offset_start + ut * half_width, # Top-left
center + u * offset_start - ut * half_width, # Bottom-left
center + u * offset_end - ut * half_width, # Bottom-right
center + u * offset_end + ut * half_width, # Top-right
]
return np.array(stripe_corners)
def plot_speed_bumps(
x_coords: Union[float, np.ndarray],
y_coords: Union[float, np.ndarray],
segment_lengths: Union[float, torch.Tensor],
segment_widths: Union[float, torch.Tensor],
segment_orientations: Union[float, torch.Tensor],
ax: matplotlib.axes.Axes,
facecolor: str = None,
edgecolor: str = None,
alpha: float = None,
) -> None:
facecolor = "xkcd:goldenrod"
edgecolor = "xkcd:black"
alpha = 0.5
for x, y, length, width, orientation in zip(
x_coords,
y_coords,
segment_lengths,
segment_widths,
segment_orientations,
):
# method1: from waymax using hatch as diagonals
points = get_corners_polygon(x, y, length, width, orientation)
p = Polygon(
points,
facecolor=facecolor,
edgecolor=edgecolor,
linewidth=0,
alpha=alpha,
hatch=r"//",
zorder=2,
)
ax.add_patch(p)
pass
def plot_stop_sign(
point: np.ndarray,
ax: matplotlib.axes.Axes,
radius: float = None,
facecolor: str = None,
edgecolor: str = None,
linewidth: float = None,
alpha: float = None,
) -> None:
# Default configurations for the stop sign
facecolor = "#c04000" if facecolor is None else facecolor
edgecolor = "white" if edgecolor is None else edgecolor
linewidth = 1.5 if linewidth is None else linewidth
radius = 1.0 if radius is None else radius
alpha = 1.0 if alpha is None else alpha
point = np.array(point).reshape(-1)
p = RegularPolygon(
point,
numVertices=6, # For hexagonal stop sign
radius=radius,
facecolor=facecolor,
edgecolor=edgecolor,
linewidth=linewidth,
alpha=alpha,
zorder=2,
)
ax.add_patch(p)
def plot_crosswalk(
points,
ax: plt.Axes = None,
facecolor: str = None,
edgecolor: str = None,
alpha: float = None,
):
if ax is None:
ax = plt.gca()
# override default config
facecolor = (
crosswalk_config["facecolor"] if facecolor is None else facecolor
)
edgecolor = (
crosswalk_config["edgecolor"] if edgecolor is None else edgecolor
)
alpha = crosswalk_config["alpha"] if alpha is None else alpha
p = Polygon(
points,
facecolor=facecolor,
edgecolor=edgecolor,
linewidth=2,
alpha=alpha,
hatch=r"//",
zorder=1,
)
ax.add_patch(p)
def plot_numpy_bounding_boxes_multiple_policy(
ax: matplotlib.axes.Axes,
bboxes_s: List[np.ndarray],
colors: List[np.ndarray],
alpha: Optional[float] = 1.0,
line_width_scale: float = 1.5,
as_center_pts: bool = False,
label: Optional[str] = None,
) -> None:
"""Plots multiple bounding boxes.
Args:
ax: Fig handles.
bboxes_s: Shape (num_policies,bboxes)
bboxes: Shape (num_bbox, 5), with last dimension as (x, y, length, width,
yaw).
colors: (num_policies,color)
color: Shape (3,), represents RGB color for drawing.
alpha: Alpha value for drawing, i.e. 0 means fully transparent.
as_center_pts: If set to True, bboxes will be drawn as center points,
instead of full bboxes.
label: String, represents the meaning of the color for different boxes.
"""
for bboxes,color in zip(bboxes_s,colors):
if bboxes.ndim != 2 or bboxes.shape[1] != 5:
raise ValueError(
(
"Expect bboxes rank 2, last dimension of bbox 5"
" got{}, {}, {} respectively"
).format(bboxes.ndim, bboxes.shape[1], color.shape)
)
if as_center_pts:
ax.plot(
bboxes[:, 0],
bboxes[:, 1],
"o",
color=color,
ms=2,
alpha=alpha,
linewidth=1.7 * line_width_scale,
label=label,
)
else:
c = np.cos(bboxes[:, 4])
s = np.sin(bboxes[:, 4])
pt = np.array((bboxes[:, 0], bboxes[:, 1])) # (2, N)
length, width = bboxes[:, 2], bboxes[:, 3]
u = np.array((c, s))
ut = np.array((s, -c))
# Compute box corner coordinates.
tl = pt + length / 2 * u - width / 2 * ut
tr = pt + length / 2 * u + width / 2 * ut
br = pt - length / 2 * u + width / 2 * ut
bl = pt - length / 2 * u - width / 2 * ut
# Compute heading arrow using center left/right/front.
cl = pt - width / 2 * ut
cr = pt + width / 2 * ut
cf = pt + length / 2 * u
# Draw bboxes.
ax.plot(
[tl[0, :], tr[0, :], br[0, :], bl[0, :], tl[0, :]],
[tl[1, :], tr[1, :], br[1, :], bl[1, :], tl[1, :]],
color=color,
zorder=4,
linewidth=1.7 * line_width_scale,
alpha=alpha,
label=label,
)
# Draw heading arrow.
ax.plot(
[cl[0, :], cr[0, :], cf[0, :], cl[0, :]],
[cl[1, :], cr[1, :], cf[1, :], cl[1, :]],
color=color,
zorder=4,
alpha=alpha,
linewidth=1.5 * line_width_scale,
label=label,
) |