openpilot1/selfdrive/modeld/dmonitoringmodeld.py

#!/usr/bin/env python3
import os
import gc
import math
import time
import ctypes
import numpy as np
from pathlib import Path

from cereal import messaging
from cereal.messaging import PubMaster, SubMaster
from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
from openpilot.common.swaglog import cloudlog
from openpilot.common.params import Params
from openpilot.common.realtime import set_realtime_priority
from openpilot.selfdrive.modeld.runners import ModelRunner, Runtime
from openpilot.selfdrive.modeld.parse_model_outputs import sigmoid

CALIB_LEN = 3
REG_SCALE = 0.25
MODEL_WIDTH = 1440
MODEL_HEIGHT = 960
OUTPUT_SIZE = 84
SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
MODEL_PATHS = {
  ModelRunner.SNPE: Path(__file__).parent / 'models/dmonitoring_model_q.dlc',
  ModelRunner.ONNX: Path(__file__).parent / 'models/dmonitoring_model.onnx'}

class DriverStateResult(ctypes.Structure):
  _fields_ = [
    ("face_orientation", ctypes.c_float*3),
    ("face_position", ctypes.c_float*3),
    ("face_orientation_std", ctypes.c_float*3),
    ("face_position_std", ctypes.c_float*3),
    ("face_prob", ctypes.c_float),
    ("_unused_a", ctypes.c_float*8),
    ("left_eye_prob", ctypes.c_float),
    ("_unused_b", ctypes.c_float*8),
    ("right_eye_prob", ctypes.c_float),
    ("left_blink_prob", ctypes.c_float),
    ("right_blink_prob", ctypes.c_float),
    ("sunglasses_prob", ctypes.c_float),
    ("occluded_prob", ctypes.c_float),
    ("ready_prob", ctypes.c_float*4),
    ("not_ready_prob", ctypes.c_float*2)]

class DMonitoringModelResult(ctypes.Structure):
  _fields_ = [
    ("driver_state_lhd", DriverStateResult),
    ("driver_state_rhd", DriverStateResult),
    ("poor_vision_prob", ctypes.c_float),
    ("wheel_on_right_prob", ctypes.c_float)]

class ModelState:
  inputs: dict[str, np.ndarray]
  output: np.ndarray
  model: ModelRunner

  def __init__(self):
    assert ctypes.sizeof(DMonitoringModelResult) == OUTPUT_SIZE * ctypes.sizeof(ctypes.c_float)
    self.output = np.zeros(OUTPUT_SIZE, dtype=np.float32)
    self.inputs = {
      'input_img': np.zeros(MODEL_HEIGHT * MODEL_WIDTH, dtype=np.uint8),
      'calib': np.zeros(CALIB_LEN, dtype=np.float32)}

    self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.DSP, True, None)
    self.model.addInput("input_img", None)
    self.model.addInput("calib", self.inputs['calib'])

  def run(self, buf:VisionBuf, calib:np.ndarray) -> tuple[np.ndarray, float]:
    self.inputs['calib'][:] = calib

    v_offset = buf.height - MODEL_HEIGHT
    h_offset = (buf.width - MODEL_WIDTH) // 2
    buf_data = buf.data.reshape(-1, buf.stride)
    input_data = self.inputs['input_img'].reshape(MODEL_HEIGHT, MODEL_WIDTH)
    input_data[:] = buf_data[v_offset:v_offset+MODEL_HEIGHT, h_offset:h_offset+MODEL_WIDTH]

    self.model.setInputBuffer("input_img", self.inputs['input_img'].view(np.float32))
    t1 = time.perf_counter()
    self.model.execute()
    t2 = time.perf_counter()
    return self.output, t2 - t1


def fill_driver_state(msg, ds_result: DriverStateResult):
  msg.faceOrientation = [x * REG_SCALE for x in ds_result.face_orientation]
  msg.faceOrientationStd = [math.exp(x) for x in ds_result.face_orientation_std]
  msg.facePosition = [x * REG_SCALE for x in ds_result.face_position[:2]]
  msg.facePositionStd = [math.exp(x) for x in ds_result.face_position_std[:2]]
  msg.faceProb = float(sigmoid(ds_result.face_prob))
  msg.leftEyeProb = float(sigmoid(ds_result.left_eye_prob))
  msg.rightEyeProb = float(sigmoid(ds_result.right_eye_prob))
  msg.leftBlinkProb = float(sigmoid(ds_result.left_blink_prob))
  msg.rightBlinkProb = float(sigmoid(ds_result.right_blink_prob))
  msg.sunglassesProb = float(sigmoid(ds_result.sunglasses_prob))
  msg.occludedProb = float(sigmoid(ds_result.occluded_prob))
  msg.readyProb = [float(sigmoid(x)) for x in ds_result.ready_prob]
  msg.notReadyProb = [float(sigmoid(x)) for x in ds_result.not_ready_prob]

def get_driverstate_packet(model_output: np.ndarray, frame_id: int, location_ts: int, execution_time: float, dsp_execution_time: float):
  model_result = ctypes.cast(model_output.ctypes.data, ctypes.POINTER(DMonitoringModelResult)).contents
  msg = messaging.new_message('driverStateV2', valid=True)
  ds = msg.driverStateV2
  ds.frameId = frame_id
  ds.modelExecutionTime = execution_time
  ds.dspExecutionTime = dsp_execution_time
  ds.poorVisionProb = float(sigmoid(model_result.poor_vision_prob))
  ds.wheelOnRightProb = float(sigmoid(model_result.wheel_on_right_prob))
  ds.rawPredictions = model_output.tobytes() if SEND_RAW_PRED else b''
  fill_driver_state(ds.leftDriverData, model_result.driver_state_lhd)
  fill_driver_state(ds.rightDriverData, model_result.driver_state_rhd)
  return msg


def main():
  gc.disable()
  set_realtime_priority(1)

  model = ModelState()
  cloudlog.warning("models loaded, dmonitoringmodeld starting")
  Params().put_bool("DmModelInitialized", True)

  cloudlog.warning("connecting to driver stream")
  vipc_client = VisionIpcClient("camerad", VisionStreamType.VISION_STREAM_DRIVER, True)
  while not vipc_client.connect(False):
    time.sleep(0.1)
  assert vipc_client.is_connected()
  cloudlog.warning(f"connected with buffer size: {vipc_client.buffer_len}")

  sm = SubMaster(["liveCalibration"])
  pm = PubMaster(["driverStateV2"])

  calib = np.zeros(CALIB_LEN, dtype=np.float32)
  # last = 0

  while True:
    buf = vipc_client.recv()
    if buf is None:
      continue

    sm.update(0)
    if sm.updated["liveCalibration"]:
      calib[:] = np.array(sm["liveCalibration"].rpyCalib)

    t1 = time.perf_counter()
    model_output, dsp_execution_time = model.run(buf, calib)
    t2 = time.perf_counter()

    pm.send("driverStateV2", get_driverstate_packet(model_output, vipc_client.frame_id, vipc_client.timestamp_sof, t2 - t1, dsp_execution_time))
    # print("dmonitoring process: %.2fms, from last %.2fms\n" % (t2 - t1, t1 - last))
    # last = t1


if __name__ == "__main__":
  main()