dragonpilot/selfdrive/locationd/calibrationd.py

#!/usr/bin/env python3
'''
This process finds calibration values. More info on what these calibration values
are can be found here https://github.com/commaai/openpilot/tree/master/common/transformations
While the roll calibration is a real value that can be estimated, here we assume it's zero,
and the image input into the neural network is not corrected for roll.
'''

import gc
import os
import capnp
import numpy as np
from typing import List, NoReturn, Optional

from cereal import log
import cereal.messaging as messaging
from common.conversions import Conversions as CV
from common.params import Params, put_nonblocking
from common.realtime import set_realtime_priority
from common.transformations.orientation import rot_from_euler, euler_from_rot
from system.swaglog import cloudlog

MIN_SPEED_FILTER = 15 * CV.MPH_TO_MS
MAX_VEL_ANGLE_STD = np.radians(0.25)
MAX_YAW_RATE_FILTER = np.radians(2)  # per second

MAX_HEIGHT_STD = np.exp(-3.5)

# This is at model frequency, blocks needed for efficiency
SMOOTH_CYCLES = 10
BLOCK_SIZE = 100
INPUTS_NEEDED = 5   # Minimum blocks needed for valid calibration
INPUTS_WANTED = 50   # We want a little bit more than we need for stability
MAX_ALLOWED_SPREAD = np.radians(2)
RPY_INIT = np.array([0.0,0.0,0.0])
WIDE_FROM_DEVICE_EULER_INIT = np.array([0.0, 0.0, 0.0])
HEIGHT_INIT = np.array([1.22])

# These values are needed to accommodate the model frame in the narrow cam of the C3
PITCH_LIMITS = np.array([-0.09074112085129739, 0.17])
YAW_LIMITS = np.array([-0.06912048084718224, 0.06912048084718235])
DEBUG = os.getenv("DEBUG") is not None


def is_calibration_valid(rpy: np.ndarray) -> bool:
  return (PITCH_LIMITS[0] < rpy[1] < PITCH_LIMITS[1]) and (YAW_LIMITS[0] < rpy[2] < YAW_LIMITS[1])  # type: ignore


def sanity_clip(rpy: np.ndarray) -> np.ndarray:
  if np.isnan(rpy).any():
    rpy = RPY_INIT
  return np.array([rpy[0],
                   np.clip(rpy[1], PITCH_LIMITS[0] - .005, PITCH_LIMITS[1] + .005),
                   np.clip(rpy[2], YAW_LIMITS[0] - .005, YAW_LIMITS[1] + .005)])

def moving_avg_with_linear_decay(prev_mean: np.ndarray, new_val: np.ndarray, idx: int, block_size: float) -> np.ndarray:
  return (idx*prev_mean + (block_size - idx) * new_val) / block_size

class Calibrator:
  def __init__(self, param_put: bool = False):
    self.param_put = param_put

    self.not_car = False

    # Read saved calibration
    params = Params()
    calibration_params = params.get("CalibrationParams")
    rpy_init = RPY_INIT
    wide_from_device_euler = WIDE_FROM_DEVICE_EULER_INIT
    height = HEIGHT_INIT
    valid_blocks = 0
    self.cal_status = log.LiveCalibrationData.Status.uncalibrated

    if param_put and calibration_params:
      try:
        with log.Event.from_bytes(calibration_params) as msg:
          rpy_init = np.array(msg.liveCalibration.rpyCalib)
          valid_blocks = msg.liveCalibration.validBlocks
          wide_from_device_euler = np.array(msg.liveCalibration.wideFromDeviceEuler)
          height = np.array(msg.liveCalibration.height)
      except Exception:
        cloudlog.exception("Error reading cached CalibrationParams")

    self.reset(rpy_init, valid_blocks, wide_from_device_euler, height)
    self.update_status()

  def reset(self, rpy_init: np.ndarray = RPY_INIT,
                  valid_blocks: int = 0,
                  wide_from_device_euler_init: np.ndarray = WIDE_FROM_DEVICE_EULER_INIT,
                  height_init: np.ndarray = HEIGHT_INIT,
                  smooth_from: Optional[np.ndarray] = None) -> None:
    if not np.isfinite(rpy_init).all():
      self.rpy = RPY_INIT.copy()
    else:
      self.rpy = rpy_init.copy()

    if not np.isfinite(height_init).all() or len(height_init) != 1:
      self.height = HEIGHT_INIT.copy()
    else:
      self.height = height_init.copy()

    if not np.isfinite(wide_from_device_euler_init).all() or len(wide_from_device_euler_init) != 3:
      self.wide_from_device_euler = WIDE_FROM_DEVICE_EULER_INIT.copy()
    else:
      self.wide_from_device_euler = wide_from_device_euler_init.copy()

    if not np.isfinite(valid_blocks) or valid_blocks < 0:
      self.valid_blocks = 0
    else:
      self.valid_blocks = valid_blocks

    self.rpys = np.tile(self.rpy, (INPUTS_WANTED, 1))
    self.wide_from_device_eulers = np.tile(self.wide_from_device_euler, (INPUTS_WANTED, 1))
    self.heights = np.tile(self.height, (INPUTS_WANTED, 1))

    self.idx = 0
    self.block_idx = 0
    self.v_ego = 0.0

    if smooth_from is None:
      self.old_rpy = RPY_INIT
      self.old_rpy_weight = 0.0
    else:
      self.old_rpy = smooth_from
      self.old_rpy_weight = 1.0

  def get_valid_idxs(self) -> List[int]:
    # exclude current block_idx from validity window
    before_current = list(range(self.block_idx))
    after_current = list(range(min(self.valid_blocks, self.block_idx + 1), self.valid_blocks))
    return before_current + after_current

  def update_status(self) -> None:
    valid_idxs = self.get_valid_idxs()
    if valid_idxs:
      self.wide_from_device_euler = np.mean(self.wide_from_device_eulers[valid_idxs], axis=0)
      self.height = np.mean(self.heights[valid_idxs], axis=0)
      rpys = self.rpys[valid_idxs]
      self.rpy = np.mean(rpys, axis=0)
      max_rpy_calib = np.array(np.max(rpys, axis=0))
      min_rpy_calib = np.array(np.min(rpys, axis=0))
      self.calib_spread = np.abs(max_rpy_calib - min_rpy_calib)
    else:
      self.calib_spread = np.zeros(3)

    if self.valid_blocks < INPUTS_NEEDED:
      if self.cal_status == log.LiveCalibrationData.Status.recalibrating:
        self.cal_status = log.LiveCalibrationData.Status.recalibrating
      else:
        self.cal_status = log.LiveCalibrationData.Status.uncalibrated
    elif is_calibration_valid(self.rpy):
      self.cal_status = log.LiveCalibrationData.Status.calibrated
    else:
      self.cal_status = log.LiveCalibrationData.Status.invalid

    # If spread is too high, assume mounting was changed and reset to last block.
    # Make the transition smooth. Abrupt transitions are not good for feedback loop through supercombo model.
    # TODO: add height spread check with smooth transition too
    if max(self.calib_spread) > MAX_ALLOWED_SPREAD and self.cal_status == log.LiveCalibrationData.Status.calibrated:
      self.reset(self.rpys[self.block_idx - 1], valid_blocks=1, smooth_from=self.rpy)
      self.cal_status = log.LiveCalibrationData.Status.recalibrating

    write_this_cycle = (self.idx == 0) and (self.block_idx % (INPUTS_WANTED//5) == 5)
    if self.param_put and write_this_cycle:
      put_nonblocking("CalibrationParams", self.get_msg().to_bytes())

  def handle_v_ego(self, v_ego: float) -> None:
    self.v_ego = v_ego

  def get_smooth_rpy(self) -> np.ndarray:
    if self.old_rpy_weight > 0:
      return self.old_rpy_weight * self.old_rpy + (1.0 - self.old_rpy_weight) * self.rpy
    else:
      return self.rpy

  def handle_cam_odom(self, trans: List[float],
                            rot: List[float],
                            wide_from_device_euler: List[float],
                            trans_std: List[float],
                            road_transform_trans: List[float],
                            road_transform_trans_std: List[float]) -> Optional[np.ndarray]:
    self.old_rpy_weight = max(0.0, self.old_rpy_weight - 1/SMOOTH_CYCLES)

    straight_and_fast = ((self.v_ego > MIN_SPEED_FILTER) and (trans[0] > MIN_SPEED_FILTER) and (abs(rot[2]) < MAX_YAW_RATE_FILTER))
    angle_std_threshold = MAX_VEL_ANGLE_STD
    height_std_threshold = MAX_HEIGHT_STD
    rpy_certain = np.arctan2(trans_std[1], trans[0]) < angle_std_threshold
    if len(road_transform_trans_std) == 3:
      height_certain = road_transform_trans_std[2] < height_std_threshold
    else:
      height_certain = True

    certain_if_calib = (rpy_certain and height_certain) or (self.valid_blocks < INPUTS_NEEDED)
    if not (straight_and_fast and certain_if_calib):
      return None

    observed_rpy = np.array([0,
                             -np.arctan2(trans[2], trans[0]),
                             np.arctan2(trans[1], trans[0])])
    new_rpy = euler_from_rot(rot_from_euler(self.get_smooth_rpy()).dot(rot_from_euler(observed_rpy)))
    new_rpy = sanity_clip(new_rpy)

    if len(wide_from_device_euler) == 3:
      new_wide_from_device_euler = np.array(wide_from_device_euler)
    else:
      new_wide_from_device_euler = WIDE_FROM_DEVICE_EULER_INIT

    if (len(road_transform_trans) == 3):
      new_height = np.array([road_transform_trans[2]])
    else:
      new_height = HEIGHT_INIT

    self.rpys[self.block_idx] = moving_avg_with_linear_decay(self.rpys[self.block_idx], new_rpy, self.idx, float(BLOCK_SIZE))
    self.wide_from_device_eulers[self.block_idx] = moving_avg_with_linear_decay(self.wide_from_device_eulers[self.block_idx], new_wide_from_device_euler, self.idx, float(BLOCK_SIZE))
    self.heights[self.block_idx] = moving_avg_with_linear_decay(self.heights[self.block_idx], new_height, self.idx, float(BLOCK_SIZE))

    self.idx = (self.idx + 1) % BLOCK_SIZE
    if self.idx == 0:
      self.block_idx += 1
      self.valid_blocks = max(self.block_idx, self.valid_blocks)
      self.block_idx = self.block_idx % INPUTS_WANTED

    self.update_status()

    return new_rpy

  def get_msg(self) -> capnp.lib.capnp._DynamicStructBuilder:
    smooth_rpy = self.get_smooth_rpy()

    msg = messaging.new_message('liveCalibration')
    liveCalibration = msg.liveCalibration

    liveCalibration.validBlocks = self.valid_blocks
    liveCalibration.calStatus = self.cal_status
    liveCalibration.calPerc = min(100 * (self.valid_blocks * BLOCK_SIZE + self.idx) // (INPUTS_NEEDED * BLOCK_SIZE), 100)
    liveCalibration.rpyCalib = smooth_rpy.tolist()
    liveCalibration.rpyCalibSpread = self.calib_spread.tolist()
    liveCalibration.wideFromDeviceEuler = self.wide_from_device_euler.tolist()
    liveCalibration.height = self.height.tolist()

    if self.not_car:
      liveCalibration.validBlocks = INPUTS_NEEDED
      liveCalibration.calStatus = log.LiveCalibrationData.Status.calibrated
      liveCalibration.calPerc = 100.
      liveCalibration.rpyCalib = [0, 0, 0]
      liveCalibration.rpyCalibSpread = self.calib_spread.tolist()

    return msg

  def send_data(self, pm: messaging.PubMaster) -> None:
    pm.send('liveCalibration', self.get_msg())


def calibrationd_thread(sm: Optional[messaging.SubMaster] = None, pm: Optional[messaging.PubMaster] = None) -> NoReturn:
  gc.disable()
  set_realtime_priority(1)

  if sm is None:
    sm = messaging.SubMaster(['cameraOdometry', 'carState', 'carParams'], poll=['cameraOdometry'])

  if pm is None:
    pm = messaging.PubMaster(['liveCalibration'])

  calibrator = Calibrator(param_put=True)

  while 1:
    timeout = 0 if sm.frame == -1 else 100
    sm.update(timeout)

    calibrator.not_car = sm['carParams'].notCar

    if sm.updated['cameraOdometry']:
      calibrator.handle_v_ego(sm['carState'].vEgo)
      new_rpy = calibrator.handle_cam_odom(sm['cameraOdometry'].trans,
                                           sm['cameraOdometry'].rot,
                                           sm['cameraOdometry'].wideFromDeviceEuler,
                                           sm['cameraOdometry'].transStd,
                                           sm['cameraOdometry'].roadTransformTrans,
                                           sm['cameraOdometry'].roadTransformTransStd)

      if DEBUG and new_rpy is not None:
        print('got new rpy', new_rpy)

    # 4Hz driven by cameraOdometry
    if sm.frame % 5 == 0:
      calibrator.send_data(pm)


def main(sm: Optional[messaging.SubMaster] = None, pm: Optional[messaging.PubMaster] = None) -> NoReturn:
  calibrationd_thread(sm, pm)


if __name__ == "__main__":
  main()