You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
314 lines
10 KiB
314 lines
10 KiB
import platform
|
|
from collections import namedtuple
|
|
|
|
import matplotlib
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import pygame
|
|
|
|
from tools.lib.lazy_property import lazy_property
|
|
from selfdrive.config import UIParams as UP
|
|
from selfdrive.config import RADAR_TO_CAMERA
|
|
from selfdrive.controls.lib.lane_planner import (compute_path_pinv,
|
|
model_polyfit)
|
|
|
|
RED = (255, 0, 0)
|
|
GREEN = (0, 255, 0)
|
|
BLUE = (0, 0, 255)
|
|
YELLOW = (255, 255, 0)
|
|
BLACK = (0, 0, 0)
|
|
WHITE = (255, 255, 255)
|
|
|
|
_PATH_X = np.arange(192.)
|
|
_PATH_XD = np.arange(192.)
|
|
_PATH_PINV = compute_path_pinv(50)
|
|
#_BB_OFFSET = 290, 332
|
|
_BB_OFFSET = 0,0
|
|
_BB_SCALE = 1164/640.
|
|
_BB_TO_FULL_FRAME = np.asarray([
|
|
[_BB_SCALE, 0., _BB_OFFSET[0]],
|
|
[0., _BB_SCALE, _BB_OFFSET[1]],
|
|
[0., 0., 1.]])
|
|
_FULL_FRAME_TO_BB = np.linalg.inv(_BB_TO_FULL_FRAME)
|
|
|
|
METER_WIDTH = 20
|
|
|
|
ModelUIData = namedtuple("ModelUIData", ["cpath", "lpath", "rpath", "lead", "lead_future"])
|
|
|
|
_COLOR_CACHE = {}
|
|
def find_color(lidar_surface, color):
|
|
if color in _COLOR_CACHE:
|
|
return _COLOR_CACHE[color]
|
|
tcolor = 0
|
|
ret = 255
|
|
for x in lidar_surface.get_palette():
|
|
#print tcolor, x
|
|
if x[0:3] == color:
|
|
ret = tcolor
|
|
break
|
|
tcolor += 1
|
|
_COLOR_CACHE[color] = ret
|
|
return ret
|
|
|
|
def warp_points(pt_s, warp_matrix):
|
|
# pt_s are the source points, nxm array.
|
|
pt_d = np.dot(warp_matrix[:, :-1], pt_s.T) + warp_matrix[:, -1, None]
|
|
|
|
# Divide by last dimension for representation in image space.
|
|
return (pt_d[:-1, :] / pt_d[-1, :]).T
|
|
|
|
def to_lid_pt(y, x):
|
|
px, py = -x * UP.lidar_zoom + UP.lidar_car_x, -y * UP.lidar_zoom + UP.lidar_car_y
|
|
if px > 0 and py > 0 and px < UP.lidar_x and py < UP.lidar_y:
|
|
return int(px), int(py)
|
|
return -1, -1
|
|
|
|
|
|
def draw_path(y, x, color, img, calibration, top_down, lid_color=None):
|
|
# TODO: Remove big box.
|
|
uv_model_real = warp_points(np.column_stack((x, y)), calibration.car_to_model)
|
|
uv_model = np.round(uv_model_real).astype(int)
|
|
|
|
uv_model_dots = uv_model[np.logical_and.reduce((np.all( # pylint: disable=no-member
|
|
uv_model > 0, axis=1), uv_model[:, 0] < img.shape[1] - 1, uv_model[:, 1] <
|
|
img.shape[0] - 1))]
|
|
|
|
for i, j in ((-1, 0), (0, -1), (0, 0), (0, 1), (1, 0)):
|
|
img[uv_model_dots[:, 1] + i, uv_model_dots[:, 0] + j] = color
|
|
|
|
# draw lidar path point on lidar
|
|
# find color in 8 bit
|
|
if lid_color is not None and top_down is not None:
|
|
tcolor = find_color(top_down[0], lid_color)
|
|
for i in range(len(x)):
|
|
px, py = to_lid_pt(x[i], y[i])
|
|
if px != -1:
|
|
top_down[1][px, py] = tcolor
|
|
|
|
def draw_steer_path(speed_ms, curvature, color, img,
|
|
calibration, top_down, VM, lid_color=None):
|
|
path_x = np.arange(101.)
|
|
path_y = np.multiply(path_x, np.tan(np.arcsin(np.clip(path_x * curvature, -0.999, 0.999)) / 2.))
|
|
|
|
draw_path(path_y, path_x, color, img, calibration, top_down, lid_color)
|
|
|
|
def draw_lead_car(closest, top_down):
|
|
if closest != None:
|
|
closest_y = int(round(UP.lidar_car_y - closest * UP.lidar_zoom))
|
|
if closest_y > 0:
|
|
top_down[1][int(round(UP.lidar_car_x - METER_WIDTH * 2)):int(
|
|
round(UP.lidar_car_x + METER_WIDTH * 2)), closest_y] = find_color(
|
|
top_down[0], (255, 0, 0))
|
|
|
|
def draw_lead_on(img, closest_x_m, closest_y_m, calibration, color, sz=10, img_offset=(0, 0)):
|
|
uv = warp_points(np.asarray([closest_x_m, closest_y_m]), calibration.car_to_bb)[0]
|
|
u, v = int(uv[0] + img_offset[0]), int(uv[1] + img_offset[1])
|
|
if u > 0 and u < 640 and v > 0 and v < 480 - 5:
|
|
img[v - 5 - sz:v - 5 + sz, u] = color
|
|
img[v - 5, u - sz:u + sz] = color
|
|
return u, v
|
|
|
|
|
|
if platform.system() != 'Darwin':
|
|
matplotlib.use('QT4Agg')
|
|
|
|
|
|
def init_plots(arr, name_to_arr_idx, plot_xlims, plot_ylims, plot_names, plot_colors, plot_styles, bigplots=False):
|
|
color_palette = { "r": (1,0,0),
|
|
"g": (0,1,0),
|
|
"b": (0,0,1),
|
|
"k": (0,0,0),
|
|
"y": (1,1,0),
|
|
"p": (0,1,1),
|
|
"m": (1,0,1) }
|
|
|
|
if bigplots == True:
|
|
fig = plt.figure(figsize=(6.4, 7.0))
|
|
elif bigplots == False:
|
|
fig = plt.figure()
|
|
else:
|
|
fig = plt.figure(figsize=bigplots)
|
|
|
|
fig.set_facecolor((0.2,0.2,0.2))
|
|
|
|
axs = []
|
|
for pn in range(len(plot_ylims)):
|
|
ax = fig.add_subplot(len(plot_ylims),1,len(axs)+1)
|
|
ax.set_xlim(plot_xlims[pn][0], plot_xlims[pn][1])
|
|
ax.set_ylim(plot_ylims[pn][0], plot_ylims[pn][1])
|
|
ax.patch.set_facecolor((0.4, 0.4, 0.4))
|
|
axs.append(ax)
|
|
|
|
plots = [] ;idxs = [] ;plot_select = []
|
|
for i, pl_list in enumerate(plot_names):
|
|
for j, item in enumerate(pl_list):
|
|
plot, = axs[i].plot(arr[:, name_to_arr_idx[item]],
|
|
label=item,
|
|
color=color_palette[plot_colors[i][j]],
|
|
linestyle=plot_styles[i][j])
|
|
plots.append(plot)
|
|
idxs.append(name_to_arr_idx[item])
|
|
plot_select.append(i)
|
|
axs[i].set_title(", ".join("%s (%s)" % (nm, cl)
|
|
for (nm, cl) in zip(pl_list, plot_colors[i])), fontsize=10)
|
|
if i < len(plot_ylims) - 1:
|
|
axs[i].set_xticks([])
|
|
|
|
fig.canvas.draw()
|
|
|
|
renderer = fig.canvas.get_renderer()
|
|
|
|
if matplotlib.get_backend() == "MacOSX":
|
|
fig.draw(renderer)
|
|
|
|
def draw_plots(arr):
|
|
for ax in axs:
|
|
ax.draw_artist(ax.patch)
|
|
for i in range(len(plots)):
|
|
plots[i].set_ydata(arr[:, idxs[i]])
|
|
axs[plot_select[i]].draw_artist(plots[i])
|
|
|
|
if matplotlib.get_backend() == "QT4Agg":
|
|
fig.canvas.update()
|
|
fig.canvas.flush_events()
|
|
|
|
raw_data = renderer.tostring_rgb()
|
|
#print fig.canvas.get_width_height()
|
|
plot_surface = pygame.image.frombuffer(raw_data, fig.canvas.get_width_height(), "RGB").convert()
|
|
return plot_surface
|
|
|
|
return draw_plots
|
|
|
|
|
|
def draw_mpc(liveMpc, top_down):
|
|
mpc_color = find_color(top_down[0], (0, 255, 0))
|
|
for p in zip(liveMpc.x, liveMpc.y):
|
|
px, py = to_lid_pt(*p)
|
|
top_down[1][px, py] = mpc_color
|
|
|
|
|
|
|
|
class CalibrationTransformsForWarpMatrix(object):
|
|
def __init__(self, model_to_full_frame, K, E):
|
|
self._model_to_full_frame = model_to_full_frame
|
|
self._K = K
|
|
self._E = E
|
|
|
|
@property
|
|
def model_to_bb(self):
|
|
return _FULL_FRAME_TO_BB.dot(self._model_to_full_frame)
|
|
|
|
@lazy_property
|
|
def model_to_full_frame(self):
|
|
return self._model_to_full_frame
|
|
|
|
@lazy_property
|
|
def car_to_model(self):
|
|
return np.linalg.inv(self._model_to_full_frame).dot(self._K).dot(
|
|
self._E[:, [0, 1, 3]])
|
|
|
|
@lazy_property
|
|
def car_to_bb(self):
|
|
return _BB_TO_FULL_FRAME.dot(self._K).dot(self._E[:, [0, 1, 3]])
|
|
|
|
|
|
def pygame_modules_have_loaded():
|
|
return pygame.display.get_init() and pygame.font.get_init()
|
|
|
|
def draw_var(y, x, var, color, img, calibration, top_down):
|
|
# otherwise drawing gets stupid
|
|
var = max(1e-1, min(var, 0.7))
|
|
|
|
varcolor = tuple(np.array(color)*0.5)
|
|
draw_path(y - var, x, varcolor, img, calibration, top_down)
|
|
draw_path(y + var, x, varcolor, img, calibration, top_down)
|
|
|
|
|
|
class ModelPoly(object):
|
|
def __init__(self, model_path):
|
|
if len(model_path.points) == 0 and len(model_path.poly) == 0:
|
|
self.valid = False
|
|
return
|
|
|
|
if len(model_path.poly):
|
|
self.poly = np.array(model_path.poly)
|
|
else:
|
|
self.poly = model_polyfit(model_path.points, _PATH_PINV)
|
|
|
|
self.prob = model_path.prob
|
|
self.std = model_path.std
|
|
self.y = np.polyval(self.poly, _PATH_XD)
|
|
self.valid = True
|
|
|
|
def extract_model_data(md):
|
|
return ModelUIData(
|
|
cpath=ModelPoly(md.path),
|
|
lpath=ModelPoly(md.leftLane),
|
|
rpath=ModelPoly(md.rightLane),
|
|
lead=md.lead,
|
|
lead_future=md.leadFuture,
|
|
)
|
|
|
|
def plot_model(m, VM, v_ego, curvature, imgw, calibration, top_down, d_poly, top_down_color=216):
|
|
if calibration is None or top_down is None:
|
|
return
|
|
|
|
for lead in [m.lead, m.lead_future]:
|
|
if lead.prob < 0.5:
|
|
continue
|
|
|
|
lead_dist_from_radar = lead.dist - RADAR_TO_CAMERA
|
|
_, py_top = to_lid_pt(lead_dist_from_radar + lead.std, lead.relY)
|
|
px, py_bottom = to_lid_pt(lead_dist_from_radar - lead.std, lead.relY)
|
|
top_down[1][int(round(px - 4)):int(round(px + 4)), py_top:py_bottom] = top_down_color
|
|
|
|
color = (0, int(255 * m.lpath.prob), 0)
|
|
for path in [m.cpath, m.lpath, m.rpath]:
|
|
if path.valid:
|
|
draw_path(path.y, _PATH_XD, color, imgw, calibration, top_down, YELLOW)
|
|
draw_var(path.y, _PATH_XD, path.std, color, imgw, calibration, top_down)
|
|
|
|
if d_poly is not None:
|
|
dpath_y = np.polyval(d_poly, _PATH_X)
|
|
draw_path(dpath_y, _PATH_X, RED, imgw, calibration, top_down, RED)
|
|
|
|
# draw user path from curvature
|
|
draw_steer_path(v_ego, curvature, BLUE, imgw, calibration, top_down, VM, BLUE)
|
|
|
|
|
|
def maybe_update_radar_points(lt, lid_overlay):
|
|
ar_pts = []
|
|
if lt is not None:
|
|
ar_pts = {}
|
|
for track in lt:
|
|
ar_pts[track.trackId] = [track.dRel, track.yRel, track.vRel, track.aRel, track.oncoming, track.stationary]
|
|
for ids, pt in ar_pts.items():
|
|
px, py = to_lid_pt(pt[0], pt[1])
|
|
if px != -1:
|
|
if pt[-1]:
|
|
color = 240
|
|
elif pt[-2]:
|
|
color = 230
|
|
else:
|
|
color = 255
|
|
if int(ids) == 1:
|
|
lid_overlay[px - 2:px + 2, py - 10:py + 10] = 100
|
|
else:
|
|
lid_overlay[px - 2:px + 2, py - 2:py + 2] = color
|
|
|
|
def get_blank_lid_overlay(UP):
|
|
lid_overlay = np.zeros((UP.lidar_x, UP.lidar_y), 'uint8')
|
|
# Draw the car.
|
|
lid_overlay[int(round(UP.lidar_car_x - UP.car_hwidth)):int(
|
|
round(UP.lidar_car_x + UP.car_hwidth)), int(round(UP.lidar_car_y -
|
|
UP.car_front))] = UP.car_color
|
|
lid_overlay[int(round(UP.lidar_car_x - UP.car_hwidth)):int(
|
|
round(UP.lidar_car_x + UP.car_hwidth)), int(round(UP.lidar_car_y +
|
|
UP.car_back))] = UP.car_color
|
|
lid_overlay[int(round(UP.lidar_car_x - UP.car_hwidth)), int(
|
|
round(UP.lidar_car_y - UP.car_front)):int(round(
|
|
UP.lidar_car_y + UP.car_back))] = UP.car_color
|
|
lid_overlay[int(round(UP.lidar_car_x + UP.car_hwidth)), int(
|
|
round(UP.lidar_car_y - UP.car_front)):int(round(
|
|
UP.lidar_car_y + UP.car_back))] = UP.car_color
|
|
return lid_overlay
|
|
|