jyoung/common/transformations/model.py

import numpy as np

from common.transformations.camera import eon_focal_length, \
	vp_from_ke, \
	get_view_frame_from_road_frame, \
	FULL_FRAME_SIZE

# segnet

SEGNET_SIZE = (512, 384)

segnet_frame_from_camera_frame = np.array([
  [float(SEGNET_SIZE[0])/FULL_FRAME_SIZE[0],    0.,          ],
  [     0.,          float(SEGNET_SIZE[1])/FULL_FRAME_SIZE[1]]])


# model

MODEL_INPUT_SIZE = (320, 160)
MODEL_YUV_SIZE = (MODEL_INPUT_SIZE[0], MODEL_INPUT_SIZE[1] * 3 // 2)
MODEL_CX = MODEL_INPUT_SIZE[0]/2.
MODEL_CY = 21.

model_zoom = 1.25
model_height = 1.22

# canonical model transform
model_intrinsics = np.array(
  [[ eon_focal_length / model_zoom,    0. ,  MODEL_CX],
   [   0. ,  eon_focal_length / model_zoom,  MODEL_CY],
   [   0. ,                            0. ,   1.]])


# BIG model

BIGMODEL_INPUT_SIZE = (864, 288)
BIGMODEL_YUV_SIZE = (BIGMODEL_INPUT_SIZE[0], BIGMODEL_INPUT_SIZE[1] * 3 // 2)

bigmodel_zoom = 1.
bigmodel_intrinsics = np.array(
  [[ eon_focal_length / bigmodel_zoom,    0. , 0.5 * BIGMODEL_INPUT_SIZE[0]],
   [   0. ,  eon_focal_length / bigmodel_zoom,  0.2 * BIGMODEL_INPUT_SIZE[1]],
   [   0. ,                            0. ,   1.]])


bigmodel_border = np.array([
    [0,0,1],
    [BIGMODEL_INPUT_SIZE[0], 0, 1],
    [BIGMODEL_INPUT_SIZE[0], BIGMODEL_INPUT_SIZE[1], 1],
    [0, BIGMODEL_INPUT_SIZE[1], 1],
])


model_frame_from_road_frame = np.dot(model_intrinsics,
  get_view_frame_from_road_frame(0, 0, 0, model_height))

bigmodel_frame_from_road_frame = np.dot(bigmodel_intrinsics,
  get_view_frame_from_road_frame(0, 0, 0, model_height))

model_frame_from_bigmodel_frame = np.dot(model_intrinsics, np.linalg.inv(bigmodel_intrinsics))

# 'camera from model camera'
def get_model_height_transform(camera_frame_from_road_frame, height):
  camera_frame_from_road_ground = np.dot(camera_frame_from_road_frame, np.array([
      [1, 0, 0],
      [0, 1, 0],
      [0, 0, 0],
      [0, 0, 1],
  ]))

  camera_frame_from_road_high = np.dot(camera_frame_from_road_frame, np.array([
      [1, 0, 0],
      [0, 1, 0],
      [0, 0, height - model_height],
      [0, 0, 1],
  ]))

  ground_from_camera_frame = np.linalg.inv(camera_frame_from_road_ground)

  low_camera_from_high_camera = np.dot(camera_frame_from_road_high, ground_from_camera_frame)
  high_camera_from_low_camera = np.linalg.inv(low_camera_from_high_camera)

  return high_camera_from_low_camera


# camera_frame_from_model_frame aka 'warp matrix'
# was: calibration.h/CalibrationTransform
def get_camera_frame_from_model_frame(camera_frame_from_road_frame, height):
  vp = vp_from_ke(camera_frame_from_road_frame)

  model_camera_from_model_frame = np.array([
    [model_zoom,         0., vp[0] - MODEL_CX * model_zoom],
    [        0., model_zoom, vp[1] - MODEL_CY * model_zoom],
    [        0.,         0.,                            1.],
  ])

  # This function is super slow, so skip it if height is very close to canonical
  # TODO: speed it up!
  if abs(height - model_height) > 0.001: #
    camera_from_model_camera = get_model_height_transform(camera_frame_from_road_frame, height)
  else:
    camera_from_model_camera = np.eye(3)

  return np.dot(camera_from_model_camera, model_camera_from_model_frame)


def get_camera_frame_from_bigmodel_frame(camera_frame_from_road_frame):
  camera_frame_from_ground = camera_frame_from_road_frame[:, (0, 1, 3)]
  bigmodel_frame_from_ground = bigmodel_frame_from_road_frame[:, (0, 1, 3)]

  ground_from_bigmodel_frame = np.linalg.inv(bigmodel_frame_from_ground)
  camera_frame_from_bigmodel_frame = np.dot(camera_frame_from_ground, ground_from_bigmodel_frame)

  return camera_frame_from_bigmodel_frame