import numpy as np
import common.transformations.orientation as orient
from selfdrive.hardware import TICI
## -- hardcoded hardware params --
eon_f_focal_length = 910.0
eon_d_focal_length = 650.0
tici_f_focal_length = 2648.0
tici_e_focal_length = tici_d_focal_length = 567.0 # probably wrong? magnification is not consistent across frame
eon_f_frame_size = (1164, 874)
eon_d_frame_size = (816, 612)
tici_f_frame_size = tici_e_frame_size = tici_d_frame_size = (1928, 1208)
# aka 'K' aka camera_frame_from_view_frame
eon_fcam_intrinsics = np.array([
[eon_f_focal_length, 0.0, float(eon_f_frame_size[0])/2],
[0.0, eon_f_focal_length, float(eon_f_frame_size[1])/2],
[0.0, 0.0, 1.0]])
eon_intrinsics = eon_fcam_intrinsics # xx
eon_dcam_intrinsics = np.array([
[eon_d_focal_length, 0.0, float(eon_d_frame_size[0])/2],
[0.0, eon_d_focal_length, float(eon_d_frame_size[1])/2],
[0.0, 0.0, 1.0]])
tici_fcam_intrinsics = np.array([
[tici_f_focal_length, 0.0, float(tici_f_frame_size[0])/2],
[0.0, tici_f_focal_length, float(tici_f_frame_size[1])/2],
[0.0, 0.0, 1.0]])
tici_dcam_intrinsics = np.array([
[tici_d_focal_length, 0.0, float(tici_d_frame_size[0])/2],
[0.0, tici_d_focal_length, float(tici_d_frame_size[1])/2],
[0.0, 0.0, 1.0]])
tici_ecam_intrinsics = tici_dcam_intrinsics
# aka 'K_inv' aka view_frame_from_camera_frame
eon_fcam_intrinsics_inv = np.linalg.inv(eon_fcam_intrinsics)
eon_intrinsics_inv = eon_fcam_intrinsics_inv # xx
tici_fcam_intrinsics_inv = np.linalg.inv(tici_fcam_intrinsics)
tici_ecam_intrinsics_inv = np.linalg.inv(tici_ecam_intrinsics)
if not TICI:
FULL_FRAME_SIZE = eon_f_frame_size
FOCAL = eon_f_focal_length
fcam_intrinsics = eon_fcam_intrinsics
else:
FULL_FRAME_SIZE = tici_f_frame_size
FOCAL = tici_f_focal_length
fcam_intrinsics = tici_fcam_intrinsics
W, H = FULL_FRAME_SIZE[0], FULL_FRAME_SIZE[1]
# device/mesh : x->forward, y-> right, z->down
# view : x->right, y->down, z->forward
device_frame_from_view_frame = np.array([
[ 0., 0., 1.],
[ 1., 0., 0.],
[ 0., 1., 0.]
])
view_frame_from_device_frame = device_frame_from_view_frame.T
def get_calib_from_vp(vp):
vp_norm = normalize(vp)
yaw_calib = np.arctan(vp_norm[0])
pitch_calib = -np.arctan(vp_norm[1]*np.cos(yaw_calib))
roll_calib = 0
return roll_calib, pitch_calib, yaw_calib
# aka 'extrinsic_matrix'
# road : x->forward, y -> left, z->up
def get_view_frame_from_road_frame(roll, pitch, yaw, height):
device_from_road = orient.rot_from_euler([roll, pitch, yaw]).dot(np.diag([1, -1, -1]))
view_from_road = view_frame_from_device_frame.dot(device_from_road)
return np.hstack((view_from_road, [[0], [height], [0]]))
# aka 'extrinsic_matrix'
def get_view_frame_from_calib_frame(roll, pitch, yaw, height):
device_from_calib= orient.rot_from_euler([roll, pitch, yaw])
view_from_calib = view_frame_from_device_frame.dot(device_from_calib)
return np.hstack((view_from_calib, [[0], [height], [0]]))
def vp_from_ke(m):
"""
Computes the vanishing point from the product of the intrinsic and extrinsic
matrices C = KE.
The vanishing point is defined as lim x->infinity C (x, 0, 0, 1).T
"""
return (m[0, 0]/m[2, 0], m[1, 0]/m[2, 0])
def vp_from_rpy(rpy, intrinsics=fcam_intrinsics):
e = get_view_frame_from_road_frame(rpy[0], rpy[1], rpy[2], 1.22)
ke = np.dot(intrinsics, e)
return vp_from_ke(ke)
def roll_from_ke(m):
# note: different from calibration.h/RollAnglefromKE: i think that one's just wrong
return np.arctan2(-(m[1, 0] - m[1, 1] * m[2, 0] / m[2, 1]),
-(m[0, 0] - m[0, 1] * m[2, 0] / m[2, 1]))
def normalize(img_pts, intrinsics=fcam_intrinsics):
# normalizes image coordinates
# accepts single pt or array of pts
intrinsics_inv = np.linalg.inv(intrinsics)
img_pts = np.array(img_pts)
input_shape = img_pts.shape
img_pts = np.atleast_2d(img_pts)
img_pts = np.hstack((img_pts, np.ones((img_pts.shape[0], 1))))
img_pts_normalized = img_pts.dot(intrinsics_inv.T)
img_pts_normalized[(img_pts < 0).any(axis=1)] = np.nan
return img_pts_normalized[:, :2].reshape(input_shape)
def denormalize(img_pts, intrinsics=fcam_intrinsics, width=np.inf, height=np.inf):
# denormalizes image coordinates
# accepts single pt or array of pts
img_pts = np.array(img_pts)
input_shape = img_pts.shape
img_pts = np.atleast_2d(img_pts)
img_pts = np.hstack((img_pts, np.ones((img_pts.shape[0], 1), dtype=img_pts.dtype)))
img_pts_denormalized = img_pts.dot(intrinsics.T)
if np.isfinite(width):
img_pts_denormalized[img_pts_denormalized[:, 0] > width] = np.nan
img_pts_denormalized[img_pts_denormalized[:, 0] < 0] = np.nan
if np.isfinite(height):
img_pts_denormalized[img_pts_denormalized[:, 1] > height] = np.nan
img_pts_denormalized[img_pts_denormalized[:, 1] < 0] = np.nan
return img_pts_denormalized[:, :2].reshape(input_shape)
def device_from_ecef(pos_ecef, orientation_ecef, pt_ecef):
# device from ecef frame
# device frame is x -> forward, y-> right, z -> down
# accepts single pt or array of pts
input_shape = pt_ecef.shape
pt_ecef = np.atleast_2d(pt_ecef)
ecef_from_device_rot = orient.rotations_from_quats(orientation_ecef)
device_from_ecef_rot = ecef_from_device_rot.T
pt_ecef_rel = pt_ecef - pos_ecef
pt_device = np.einsum('jk,ik->ij', device_from_ecef_rot, pt_ecef_rel)
return pt_device.reshape(input_shape)
def img_from_device(pt_device):
# img coordinates from pts in device frame
# first transforms to view frame, then to img coords
# accepts single pt or array of pts
input_shape = pt_device.shape
pt_device = np.atleast_2d(pt_device)
pt_view = np.einsum('jk,ik->ij', view_frame_from_device_frame, pt_device)
# This function should never return negative depths
pt_view[pt_view[:, 2] < 0] = np.nan
pt_img = pt_view/pt_view[:, 2:3]
return pt_img.reshape(input_shape)[:, :2]
def get_camera_frame_from_calib_frame(camera_frame_from_road_frame, intrinsics=fcam_intrinsics):
camera_frame_from_ground = camera_frame_from_road_frame[:, (0, 1, 3)]
calib_frame_from_ground = np.dot(intrinsics,
get_view_frame_from_road_frame(0, 0, 0, 1.22))[:, (0, 1, 3)]
ground_from_calib_frame = np.linalg.inv(calib_frame_from_ground)
camera_frame_from_calib_frame = np.dot(camera_frame_from_ground, ground_from_calib_frame)
return camera_frame_from_calib_frame