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160 lines
5.6 KiB
160 lines
5.6 KiB
import numpy as np
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import common.transformations.orientation as orient
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## -- hardcoded hardware params --
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eon_f_focal_length = 910.0
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eon_d_focal_length = 650.0
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tici_f_focal_length = 2648.0
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tici_e_focal_length = tici_d_focal_length = 567.0 # probably wrong? magnification is not consistent across frame
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eon_f_frame_size = (1164, 874)
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eon_d_frame_size = (816, 612)
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tici_f_frame_size = tici_e_frame_size = tici_d_frame_size = (1928, 1208)
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# aka 'K' aka camera_frame_from_view_frame
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eon_fcam_intrinsics = np.array([
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[eon_f_focal_length, 0.0, float(eon_f_frame_size[0])/2],
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[0.0, eon_f_focal_length, float(eon_f_frame_size[1])/2],
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[0.0, 0.0, 1.0]])
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eon_intrinsics = eon_fcam_intrinsics # xx
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eon_dcam_intrinsics = np.array([
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[eon_d_focal_length, 0.0, float(eon_d_frame_size[0])/2],
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[0.0, eon_d_focal_length, float(eon_d_frame_size[1])/2],
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[0.0, 0.0, 1.0]])
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tici_fcam_intrinsics = np.array([
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[tici_f_focal_length, 0.0, float(tici_f_frame_size[0])/2],
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[0.0, tici_f_focal_length, float(tici_f_frame_size[1])/2],
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[0.0, 0.0, 1.0]])
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tici_dcam_intrinsics = np.array([
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[tici_d_focal_length, 0.0, float(tici_d_frame_size[0])/2],
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[0.0, tici_d_focal_length, float(tici_d_frame_size[1])/2],
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[0.0, 0.0, 1.0]])
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tici_ecam_intrinsics = tici_dcam_intrinsics
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# aka 'K_inv' aka view_frame_from_camera_frame
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eon_fcam_intrinsics_inv = np.linalg.inv(eon_fcam_intrinsics)
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eon_intrinsics_inv = eon_fcam_intrinsics_inv # xx
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tici_fcam_intrinsics_inv = np.linalg.inv(tici_fcam_intrinsics)
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tici_ecam_intrinsics_inv = np.linalg.inv(tici_ecam_intrinsics)
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FULL_FRAME_SIZE = tici_f_frame_size
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FOCAL = tici_f_focal_length
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fcam_intrinsics = tici_fcam_intrinsics
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W, H = FULL_FRAME_SIZE[0], FULL_FRAME_SIZE[1]
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# device/mesh : x->forward, y-> right, z->down
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# view : x->right, y->down, z->forward
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device_frame_from_view_frame = np.array([
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[ 0., 0., 1.],
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[ 1., 0., 0.],
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[ 0., 1., 0.]
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])
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view_frame_from_device_frame = device_frame_from_view_frame.T
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def get_calib_from_vp(vp):
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vp_norm = normalize(vp)
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yaw_calib = np.arctan(vp_norm[0])
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pitch_calib = -np.arctan(vp_norm[1]*np.cos(yaw_calib))
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roll_calib = 0
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return roll_calib, pitch_calib, yaw_calib
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# aka 'extrinsic_matrix'
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# road : x->forward, y -> left, z->up
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def get_view_frame_from_road_frame(roll, pitch, yaw, height):
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device_from_road = orient.rot_from_euler([roll, pitch, yaw]).dot(np.diag([1, -1, -1]))
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view_from_road = view_frame_from_device_frame.dot(device_from_road)
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return np.hstack((view_from_road, [[0], [height], [0]]))
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# aka 'extrinsic_matrix'
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def get_view_frame_from_calib_frame(roll, pitch, yaw, height):
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device_from_calib= orient.rot_from_euler([roll, pitch, yaw])
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view_from_calib = view_frame_from_device_frame.dot(device_from_calib)
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return np.hstack((view_from_calib, [[0], [height], [0]]))
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def vp_from_ke(m):
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"""
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Computes the vanishing point from the product of the intrinsic and extrinsic
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matrices C = KE.
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The vanishing point is defined as lim x->infinity C (x, 0, 0, 1).T
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"""
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return (m[0, 0]/m[2, 0], m[1, 0]/m[2, 0])
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def roll_from_ke(m):
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# note: different from calibration.h/RollAnglefromKE: i think that one's just wrong
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return np.arctan2(-(m[1, 0] - m[1, 1] * m[2, 0] / m[2, 1]),
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-(m[0, 0] - m[0, 1] * m[2, 0] / m[2, 1]))
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def normalize(img_pts, intrinsics=fcam_intrinsics):
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# normalizes image coordinates
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# accepts single pt or array of pts
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intrinsics_inv = np.linalg.inv(intrinsics)
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img_pts = np.array(img_pts)
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input_shape = img_pts.shape
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img_pts = np.atleast_2d(img_pts)
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img_pts = np.hstack((img_pts, np.ones((img_pts.shape[0], 1))))
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img_pts_normalized = img_pts.dot(intrinsics_inv.T)
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img_pts_normalized[(img_pts < 0).any(axis=1)] = np.nan
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return img_pts_normalized[:, :2].reshape(input_shape)
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def denormalize(img_pts, intrinsics=fcam_intrinsics, width=np.inf, height=np.inf):
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# denormalizes image coordinates
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# accepts single pt or array of pts
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img_pts = np.array(img_pts)
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input_shape = img_pts.shape
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img_pts = np.atleast_2d(img_pts)
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img_pts = np.hstack((img_pts, np.ones((img_pts.shape[0], 1), dtype=img_pts.dtype)))
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img_pts_denormalized = img_pts.dot(intrinsics.T)
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if np.isfinite(width):
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img_pts_denormalized[img_pts_denormalized[:, 0] > width] = np.nan
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img_pts_denormalized[img_pts_denormalized[:, 0] < 0] = np.nan
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if np.isfinite(height):
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img_pts_denormalized[img_pts_denormalized[:, 1] > height] = np.nan
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img_pts_denormalized[img_pts_denormalized[:, 1] < 0] = np.nan
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return img_pts_denormalized[:, :2].reshape(input_shape)
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def device_from_ecef(pos_ecef, orientation_ecef, pt_ecef):
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# device from ecef frame
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# device frame is x -> forward, y-> right, z -> down
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# accepts single pt or array of pts
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input_shape = pt_ecef.shape
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pt_ecef = np.atleast_2d(pt_ecef)
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ecef_from_device_rot = orient.rotations_from_quats(orientation_ecef)
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device_from_ecef_rot = ecef_from_device_rot.T
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pt_ecef_rel = pt_ecef - pos_ecef
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pt_device = np.einsum('jk,ik->ij', device_from_ecef_rot, pt_ecef_rel)
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return pt_device.reshape(input_shape)
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def img_from_device(pt_device):
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# img coordinates from pts in device frame
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# first transforms to view frame, then to img coords
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# accepts single pt or array of pts
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input_shape = pt_device.shape
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pt_device = np.atleast_2d(pt_device)
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pt_view = np.einsum('jk,ik->ij', view_frame_from_device_frame, pt_device)
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# This function should never return negative depths
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pt_view[pt_view[:, 2] < 0] = np.nan
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pt_img = pt_view/pt_view[:, 2:3]
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return pt_img.reshape(input_shape)[:, :2]
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