openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 200 supported car makes and models.
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#!/usr/bin/env python3
import os
import numpy as np
import common.transformations.orientation as orient
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from selfdrive.locationd.kalman.helpers import (TEMPLATE_DIR, load_code,
write_code)
from selfdrive.locationd.kalman.helpers.sympy_helpers import quat_matrix_l
def sane(track):
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img_pos = track[1:, 2:4]
diffs_x = abs(img_pos[1:, 0] - img_pos[:-1, 0])
diffs_y = abs(img_pos[1:, 1] - img_pos[:-1, 1])
for i in range(1, len(diffs_x)):
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if ((diffs_x[i] > 0.05 or diffs_x[i - 1] > 0.05) and
(diffs_x[i] > 2 * diffs_x[i - 1] or
diffs_x[i] < .5 * diffs_x[i - 1])) or \
((diffs_y[i] > 0.05 or diffs_y[i - 1] > 0.05) and
(diffs_y[i] > 2 * diffs_y[i - 1] or
diffs_y[i] < .5 * diffs_y[i - 1])):
return False
return True
class FeatureHandler():
name = 'feature_handler'
@staticmethod
def generate_code(K=5):
# Wrap c code for slow matching
c_header = "\nvoid merge_features(double *tracks, double *features, long long *empty_idxs);"
c_code = "#include <math.h>\n"
c_code += "#include <string.h>\n"
c_code += "#define K %d\n" % K
c_code += "\n" + open(os.path.join(TEMPLATE_DIR, "feature_handler.c")).read()
filename = f"{FeatureHandler.name}_{K}"
write_code(filename, c_code, c_header)
def __init__(self, K=5):
self.MAX_TRACKS = 6000
self.K = K
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# Array of tracks, each track has K 5D features preceded
# by 5 params that inidicate [f_idx, last_idx, updated, complete, valid]
# f_idx: idx of current last feature in track
# idx of of last feature in frame
# bool for whether this track has been update
# bool for whether this track is complete
# bool for whether this track is valid
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self.tracks = np.zeros((self.MAX_TRACKS, K + 1, 5))
self.tracks[:] = np.nan
name = f"{FeatureHandler.name}_{K}"
ffi, lib = load_code(name)
def merge_features_c(tracks, features, empty_idxs):
lib.merge_features(ffi.cast("double *", tracks.ctypes.data),
ffi.cast("double *", features.ctypes.data),
ffi.cast("long long *", empty_idxs.ctypes.data))
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# self.merge_features = self.merge_features_python
self.merge_features = merge_features_c
def reset(self):
self.tracks[:] = np.nan
def merge_features_python(self, tracks, features, empty_idxs):
empty_idx = 0
for f in features:
match_idx = int(f[4])
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if tracks[match_idx, 0, 1] == match_idx and tracks[match_idx, 0, 2] == 0:
tracks[match_idx, 0, 0] += 1
tracks[match_idx, 0, 1] = f[1]
tracks[match_idx, 0, 2] = 1
tracks[match_idx, int(tracks[match_idx, 0, 0])] = f
if tracks[match_idx, 0, 0] == self.K:
tracks[match_idx, 0, 3] = 1
if sane(tracks[match_idx]):
tracks[match_idx, 0, 4] = 1
else:
if empty_idx == len(empty_idxs):
print('need more empty space')
continue
tracks[empty_idxs[empty_idx], 0, 0] = 1
tracks[empty_idxs[empty_idx], 0, 1] = f[1]
tracks[empty_idxs[empty_idx], 0, 2] = 1
tracks[empty_idxs[empty_idx], 1] = f
empty_idx += 1
def update_tracks(self, features):
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last_idxs = np.copy(self.tracks[:, 0, 1])
real = np.isfinite(last_idxs)
self.tracks[last_idxs[real].astype(int)] = self.tracks[real]
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mask = np.ones(self.MAX_TRACKS, np.bool)
mask[last_idxs[real].astype(int)] = 0
empty_idxs = np.arange(self.MAX_TRACKS)[mask]
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self.tracks[empty_idxs] = np.nan
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self.tracks[:, 0, 2] = 0
self.merge_features(self.tracks, features, empty_idxs)
def handle_features(self, features):
self.update_tracks(features)
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valid_idxs = self.tracks[:, 0, 4] == 1
complete_idxs = self.tracks[:, 0, 3] == 1
stale_idxs = self.tracks[:, 0, 2] == 0
valid_tracks = self.tracks[valid_idxs]
self.tracks[complete_idxs] = np.nan
self.tracks[stale_idxs] = np.nan
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return valid_tracks[:, 1:, :4].reshape((len(valid_tracks), self.K * 4))
def generate_orient_error_jac(K):
import sympy as sp
from common.sympy_helpers import quat_rotate
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x_sym = sp.MatrixSymbol('abr', 3, 1)
dtheta = sp.MatrixSymbol('dtheta', 3, 1)
delta_quat = sp.Matrix(np.ones(4))
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delta_quat[1:, :] = sp.Matrix(0.5 * dtheta[0:3, :])
poses_sym = sp.MatrixSymbol('poses', 7 * K, 1)
img_pos_sym = sp.MatrixSymbol('img_positions', 2 * K, 1)
alpha, beta, rho = x_sym
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to_c = sp.Matrix(orient.rot_matrix(-np.pi / 2, -np.pi / 2, 0))
pos_0 = sp.Matrix(np.array(poses_sym[K * 7 - 7:K * 7 - 4])[:, 0])
q = quat_matrix_l(poses_sym[K * 7 - 4:K * 7]) * delta_quat
quat_rot = quat_rotate(*q)
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rot_g_to_0 = to_c * quat_rot.T
rows = []
for i in range(K):
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pos_i = sp.Matrix(np.array(poses_sym[i * 7:i * 7 + 3])[:, 0])
q = quat_matrix_l(poses_sym[7 * i + 3:7 * i + 7]) * delta_quat
quat_rot = quat_rotate(*q)
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rot_g_to_i = to_c * quat_rot.T
rot_0_to_i = rot_g_to_i * (rot_g_to_0.T)
trans_0_to_i = rot_g_to_i * (pos_0 - pos_i)
funct_vec = rot_0_to_i * sp.Matrix([alpha, beta, 1]) + rho * trans_0_to_i
h1, h2, h3 = funct_vec
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rows.append(h1 / h3 - img_pos_sym[i * 2 + 0])
rows.append(h2 / h3 - img_pos_sym[i * 2 + 1])
img_pos_residual_sym = sp.Matrix(rows)
# sympy into c
sympy_functions = []
sympy_functions.append(('orient_error_jac', img_pos_residual_sym.jacobian(dtheta), [x_sym, poses_sym, img_pos_sym, dtheta]))
return sympy_functions
if __name__ == "__main__":
# TODO: get K from argparse
FeatureHandler.generate_code()