import numpy as np
import common.transformations.orientation as orient
import math
FULL_FRAME_SIZE = (1164, 874)
W, H = FULL_FRAME_SIZE[0], FULL_FRAME_SIZE[1]
eon_focal_length = FOCAL = 910.0
# aka 'K' aka camera_frame_from_view_frame
eon_intrinsics = np.array([
[FOCAL, 0., W/2.],
[ 0., FOCAL, H/2.],
[ 0., 0., 1.]])
leon_dcam_intrinsics = np.array([
[650, 0, 816//2],
[ 0, 650, 612//2],
[ 0, 0, 1]])
eon_dcam_intrinsics = np.array([
[860, 0, 1152//2],
[ 0, 860, 864//2],
[ 0, 0, 1]])
# aka 'K_inv' aka view_frame_from_camera_frame
eon_intrinsics_inv = np.linalg.inv(eon_intrinsics)
# 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]]))
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):
e = get_view_frame_from_road_frame(rpy[0], rpy[1], rpy[2], 1.22)
ke = np.dot(eon_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=eon_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=eon_intrinsics):
# 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))))
img_pts_denormalized = img_pts.dot(intrinsics.T)
img_pts_denormalized[img_pts_denormalized[:,0] > W] = np.nan
img_pts_denormalized[img_pts_denormalized[:,0] < 0] = np.nan
img_pts_denormalized[img_pts_denormalized[:,1] > H] = 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):
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,
output_size=None,
pretransform=None,
top_hacks=False,
yuv=False,
alpha=1.0,
beta=0,
blur=0):
import cv2 # pylint: disable=import-error
cv2.setNumThreads(1)
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]
cy = from_intr[1,2]
def get_M(h=1.22):
quadrangle = np.array([[0, cy + 20],
[size[1]-1, cy + 20],
[0, size[0]-1],
[size[1]-1, size[0]-1]], dtype=np.float32)
quadrangle_norm = np.hstack((normalize(quadrangle, intrinsics=from_intr), np.ones((4,1))))
quadrangle_world = np.column_stack((h*quadrangle_norm[:,0]/quadrangle_norm[:,1],
h*np.ones(4),
h/quadrangle_norm[:,1]))
rot = orient.rot_from_euler(augment_eulers)
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)))))
warped_quadrangle = np.column_stack((warped_quadrangle_full[:,0]/warped_quadrangle_full[:,2],
warped_quadrangle_full[:,1]/warped_quadrangle_full[:,2])).astype(np.float32)
M = cv2.getPerspectiveTransform(quadrangle, warped_quadrangle.astype(np.float32))
return M
M = get_M()
if pretransform is not None:
M = M.dot(pretransform)
augmented_rgb = cv2.warpPerspective(base_img, M, output_size, borderMode=cv2.BORDER_REPLICATE)
if top_hacks:
cyy = int(math.ceil(to_intr[1,2]))
M = get_M(1000)
if pretransform is not None:
M = M.dot(pretransform)
augmented_rgb[:cyy] = cv2.warpPerspective(base_img, M, (output_size[0], cyy), borderMode=cv2.BORDER_REPLICATE)
# 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
# center in array coordinates so row, column
import cv2 # pylint: disable=import-error
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
if not center:
center = (rgb.shape[0]/2, rgb.shape[1]/2)
rgb_crop = rgb[center[0] - output_size[1]/2: center[0] + output_size[1]/2,
center[1] - output_size[0]/2: center[1] + output_size[0]/2]
return cv2.cvtColor(rgb_crop, cv2.COLOR_RGB2YUV_I420)