openpilot v0.6.4 release

.gitignore

@@ -39,5 +39,6 @@ selfdrive/sensord/sensord
 
 one
 openpilot
+notebooks
 xx
 

RELEASES.md

@@ -1,3 +1,13 @@
+Version 0.6.4 (2019-09-08)
+========================
+ * Forward stock AEB for Honda Nidec
+ * Improve lane centering on banked roads
+ * Always-on forward collision warning
+ * Always-on driver monitoring, except for right hand drive countries
+ * Driver monitoring learns the user's normal driving position
+ * Honda Fit support thanks to energee!
+ * Lexus IS support
+
 Version 0.6.3 (2019-08-12)
 ========================
  * Alert sounds from EON: requires NEOS update

common/api/__init__.py

@@ -5,9 +5,10 @@
 from selfdrive.version import version
 
 class Api(object):
-  def __init__(self, dongle_id, private_key):
+  def __init__(self, dongle_id):
     self.dongle_id = dongle_id
-    self.private_key = private_key
+    with open('/persist/comma/id_rsa') as f:
+      self.private_key = f.read()
 
   def get(self, *args, **kwargs):
     return self.request('GET', *args, **kwargs)
@@ -19,7 +20,14 @@ def request(self, method, endpoint, timeout=None, access_token=None, **params):
     return api_get(endpoint, method=method, timeout=timeout, access_token=access_token, **params)
 
   def get_token(self):
-    return jwt.encode({'identity': self.dongle_id, 'exp': datetime.utcnow() + timedelta(hours=1)}, self.private_key, algorithm='RS256')
+    now = datetime.utcnow()
+    payload = {
+      'identity': self.dongle_id,
+      'nbf': now,
+      'iat': now,
+      'exp': now + timedelta(hours=1)
+    }
+    return jwt.encode(payload, self.private_key, algorithm='RS256')
 
 def api_get(endpoint, method='GET', timeout=None, access_token=None, **params):
   backend = "https://api.commadotai.com/"

common/file_helpers.py

@@ -24,21 +24,12 @@ def rm_tree_or_link(path):
     shutil.rmtree(path)
 
 def get_tmpdir_on_same_filesystem(path):
-  # TODO(mgraczyk): HACK, we should actually check for which filesystem.
   normpath = os.path.normpath(path)
   parts = normpath.split("/")
-  if len(parts) > 1:
-    if parts[1].startswith("raid") or parts[1].startswith("datasets"):
-      if len(parts) > 2 and parts[2] == "runner":
-        return "/{}/runner/tmp".format(parts[1])
-      elif len(parts) > 2 and parts[2] == "aws":
-        return "/{}/aws/tmp".format(parts[1])
-      else:
-        return "/{}/tmp".format(parts[1])
-    elif parts[1] == "aws":
-      return "/aws/tmp"
-    elif parts[1] == "scratch":
-      return "/scratch/tmp"
+  if len(parts) > 1 and parts[1] == "scratch":
+    return "/scratch/tmp"
+  elif len(parts) > 2 and parts[2] == "runner":
+    return "/{}/runner/tmp".format(parts[1])
   return "/tmp"
 
 class AutoMoveTempdir(object):

common/params.py

@@ -27,6 +27,7 @@
 import shutil
 import fcntl
 import tempfile
+import threading
 from enum import Enum
 
 
@@ -63,10 +64,9 @@ class UnknownKeyName(Exception):
   "GitCommit": [TxType.PERSISTENT],
   "GitRemote": [TxType.PERSISTENT],
   "HasAcceptedTerms": [TxType.PERSISTENT],
-  "IsDriverMonitoringEnabled": [TxType.PERSISTENT],
-  "IsFcwEnabled": [TxType.PERSISTENT],
   "IsGeofenceEnabled": [TxType.PERSISTENT],
   "IsMetric": [TxType.PERSISTENT],
+  "IsRHD": [TxType.PERSISTENT],
   "IsUpdateAvailable": [TxType.PERSISTENT],
   "IsUploadRawEnabled": [TxType.PERSISTENT],
   "IsUploadVideoOverCellularEnabled": [TxType.PERSISTENT],
@@ -346,6 +346,17 @@ def put(self, key, dat):
 
     write_db(self.db, key, dat)
 
+
+def put_nonblocking(key, val):
+  def f(key, val):
+    params = Params()
+    params.put(key, val)
+
+  t = threading.Thread(target=f, args=(key, val))
+  t.start()
+  return t
+
+
 if __name__ == "__main__":
   params = Params()
   if len(sys.argv) > 2:

common/stat_live.py

@@ -0,0 +1,73 @@
+import numpy as np
+
+class RunningStat():
+  # tracks realtime mean and standard deviation without storing any data
+  def __init__(self, priors=None, max_trackable=-1):
+    self.max_trackable = max_trackable
+    if priors is not None:
+      # initialize from history
+      self.M = priors[0]
+      self.S = priors[1]
+      self.n = priors[2]
+      self.M_last = self.M
+      self.S_last = self.S
+
+    else:
+      self.reset()
+
+  def reset(self):
+    self.M = 0.
+    self.S = 0.
+    self.M_last = 0.
+    self.S_last = 0.
+    self.n = 0
+
+  def push_data(self, new_data):
+    # short term memory hack
+    if self.max_trackable < 0 or self.n < self.max_trackable:
+      self.n += 1
+    if self.n == 0:
+      self.M_last = new_data
+      self.M = self.M_last
+      self.S_last = 0.
+    else:
+      self.M = self.M_last + (new_data - self.M_last) / self.n
+      self.S = self.S_last + (new_data - self.M_last) * (new_data - self.M);
+      self.M_last = self.M
+      self.S_last = self.S
+
+  def mean(self):
+    return self.M
+
+  def variance(self):
+    if self.n >= 2:
+      return self.S / (self.n - 1.)
+    else:
+      return 0
+
+  def std(self):
+    return np.sqrt(self.variance())
+
+  def params_to_save(self):
+    return [self.M, self.S, self.n]
+
+class RunningStatFilter():
+  def __init__(self, raw_priors=None, filtered_priors=None, max_trackable=-1):
+    self.raw_stat = RunningStat(raw_priors, max_trackable)
+    self.filtered_stat = RunningStat(filtered_priors, max_trackable)
+
+  def reset(self):
+    self.raw_stat.reset()
+    self.filtered_stat.reset()
+
+  def push_and_update(self, new_data):
+    _std_last = self.raw_stat.std()
+    self.raw_stat.push_data(new_data)
+    _delta_std = self.raw_stat.std() - _std_last
+    if _delta_std<=0:
+      self.filtered_stat.push_data(new_data)
+    else:
+      pass
+      # self.filtered_stat.push_data(self.filtered_stat.mean())
+
+# class SequentialBayesian():

common/transformations/camera.py

@@ -154,17 +154,40 @@ def rotate_img(img, eulers, crop=None, intrinsics=eon_intrinsics):
                     W_border: size[1] - W_border]
 
 
+def get_camera_frame_from_calib_frame(camera_frame_from_road_frame):
+  camera_frame_from_ground = camera_frame_from_road_frame[:, (0, 1, 3)]
+  calib_frame_from_ground = np.dot(eon_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
+
+
+def pretransform_from_calib(calib):
+  roll, pitch, yaw, height = calib
+  view_frame_from_road_frame = get_view_frame_from_road_frame(roll, pitch, yaw, height)
+  camera_frame_from_road_frame = np.dot(eon_intrinsics, view_frame_from_road_frame)
+  camera_frame_from_calib_frame = get_camera_frame_from_calib_frame(camera_frame_from_road_frame)
+  return np.linalg.inv(camera_frame_from_calib_frame)
+
+
 def transform_img(base_img,
                  augment_trans=np.array([0,0,0]),
                  augment_eulers=np.array([0,0,0]),
                  from_intr=eon_intrinsics,
                  to_intr=eon_intrinsics,
-                 calib_rot_view=None,
                  output_size=None,
                  pretransform=None,
-                 top_hacks=True):
+                 top_hacks=False,
+                 yuv=False,
+                 alpha=1.0,
+                 beta=0,
+                 blur=0):
   import cv2
 
+  if yuv:
+    base_img = cv2.cvtColor(base_img, cv2.COLOR_YUV2RGB_I420)
+
   size = base_img.shape[:2]
   if not output_size:
     output_size = size[::-1]
@@ -180,8 +203,6 @@ def get_M(h=1.22):
                                         h*np.ones(4),
                                         h/quadrangle_norm[:,1]))
     rot = orient.rot_from_euler(augment_eulers)
-    if calib_rot_view is not None:
-      rot = calib_rot_view.dot(rot)
     to_extrinsics = np.hstack((rot.T, -augment_trans[:,None]))
     to_KE = to_intr.dot(to_extrinsics)
     warped_quadrangle_full = np.einsum('jk,ik->ij', to_KE, np.hstack((quadrangle_world, np.ones((4,1)))))
@@ -202,7 +223,19 @@ def get_M(h=1.22):
       M = M.dot(pretransform)
     augmented_rgb[:cyy] = cv2.warpPerspective(base_img, M, (output_size[0], cyy), borderMode=cv2.BORDER_REPLICATE)
 
-  return augmented_rgb
+  # brightness and contrast augment
+  augmented_rgb = np.clip((float(alpha)*augmented_rgb + beta), 0, 255).astype(np.uint8)
+
+  # gaussian blur
+  if blur > 0:
+    augmented_rgb = cv2.GaussianBlur(augmented_rgb,(blur*2+1,blur*2+1),cv2.BORDER_DEFAULT)
+
+  if yuv:
+    augmented_img = cv2.cvtColor(augmented_rgb, cv2.COLOR_RGB2YUV_I420)
+  else:
+    augmented_img = augmented_rgb
+  return augmented_img
+
 
 def yuv_crop(frame, output_size, center=None):
   # output_size in camera coordinates so u,v

common/transformations/model.py

@@ -1,8 +1,8 @@
 import numpy as np
 
-from common.transformations.camera import eon_focal_length, \
-        vp_from_ke, get_view_frame_from_road_frame, \
-        FULL_FRAME_SIZE
+from common.transformations.camera import (FULL_FRAME_SIZE, eon_focal_length,
+                                           get_view_frame_from_road_frame,
+                                           vp_from_ke)
 
 # segnet