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|
#!/usr/bin/env python3
r'''Tests for project() and unproject()
Here I make sure the projection functions return the correct values. A part of
this is a regression test: the "right" project() results were recorded at some
point, and any deviation is flagged.
This also test gradients, normalization and in-place output.
I want to check all combinations of
add others here: latlon, lonlat, stereographic. Broadcasted and not. Test the
project() and unproject() paths
- project/unproject
- get_gradients: yes/no
- model simple: yes/no
- broadcasted: yes/no
- unproject normalize: yes/no
- explicit "out" in args
check() covers all of these for ONE model
'''
import sys
import numpy as np
import numpysane as nps
import os
testdir = os.path.dirname(os.path.realpath(__file__))
# I import the LOCAL mrcal since that's what I'm testing
sys.path[:0] = f"{testdir}/..",
import mrcal
import testutils
from test_calibration_helpers import grad
import re
def check(intrinsics, p_ref, q_ref):
########## project
q_projected = mrcal.project(p_ref, *intrinsics)
testutils.confirm_equal(q_projected,
q_ref,
msg = f"Projecting {intrinsics[0]}",
eps = 1e-2)
q_projected *= 0
mrcal.project(p_ref, *intrinsics,
out = q_projected)
testutils.confirm_equal(q_projected,
q_ref,
msg = f"Projecting {intrinsics[0]} in-place",
eps = 1e-2)
meta = mrcal.lensmodel_metadata_and_config(intrinsics[0])
if meta['has_gradients']:
@nps.broadcast_define( ((3,),('N',)) )
def grad_broadcasted(p_ref, i_ref):
return grad(lambda pi: mrcal.project(pi[:3], intrinsics[0], pi[3:]),
nps.glue(p_ref,i_ref, axis=-1))
dq_dpi_ref = grad_broadcasted(p_ref,intrinsics[1])
q_projected,dq_dp,dq_di = mrcal.project(p_ref, *intrinsics, get_gradients=True)
testutils.confirm_equal(q_projected,
q_ref,
msg = f"Projecting {intrinsics[0]} with grad",
eps = 1e-2)
testutils.confirm_equal(dq_dp,
dq_dpi_ref[...,:3],
msg = f"dq_dp {intrinsics[0]}",
eps = 1e-2)
testutils.confirm_equal(dq_di,
dq_dpi_ref[...,3:],
msg = f"dq_di {intrinsics[0]}",
eps = 1e-2)
out=[q_projected,dq_dp,dq_di]
out[0] *= 0
out[1] *= 0
out[2] *= 0
mrcal.project(p_ref, *intrinsics, get_gradients=True, out=out)
testutils.confirm_equal(q_projected,
q_ref,
msg = f"Projecting {intrinsics[0]} with grad in-place",
eps = 1e-2)
testutils.confirm_equal(dq_dp,
dq_dpi_ref[...,:3],
msg = f"dq_dp in-place",
eps = 1e-2)
testutils.confirm_equal(dq_di,
dq_dpi_ref[...,3:],
msg = f"dq_di in-place",
eps = 1e-2)
if meta['noncentral']:
if re.match('LENSMODEL_CAHVORE',intrinsics[0]):
# CAHVORE. I set E=0, and the projection becomes central. Then
# unproject() is well-defined and I can run the tests
intrinsics[1][-3:] = 0
q_projected = mrcal.project(p_ref, *intrinsics)
else:
# Some other kind of noncentral model. I don't bother with
# unproject()
return
########## unproject
if 1:
##### Un-normalized
v_unprojected = mrcal.unproject(q_projected, *intrinsics,
normalize = False)
cos = nps.inner(v_unprojected, p_ref) / nps.mag(p_ref)
cos = np.clip(cos, -1, 1)
testutils.confirm_equal( np.arccos(cos),
np.zeros((p_ref.shape[0],), dtype=float),
msg = f"Unprojecting {intrinsics[0]}",
eps = 1e-6)
if 1:
##### Normalized
v_unprojected_nograd = mrcal.unproject(q_projected, *intrinsics,
normalize = True)
testutils.confirm_equal( nps.norm2(v_unprojected_nograd),
1,
msg = f"Unprojected v are normalized",
eps = 1e-6)
cos = nps.inner(v_unprojected_nograd, p_ref) / nps.mag(p_ref)
cos = np.clip(cos, -1, 1)
testutils.confirm_equal( np.arccos(cos),
np.zeros((p_ref.shape[0],), dtype=float),
msg = f"Unprojecting {intrinsics[0]} (normalized)",
eps = 1e-6)
if not meta['has_gradients']:
# no in-place output for the no-gradients unproject() path
return
v_unprojected *= 0
mrcal.unproject(q_projected, *intrinsics,
normalize = True,
out = v_unprojected)
testutils.confirm_equal( nps.norm2(v_unprojected),
1,
msg = f"Unprojected in-place v are normalized",
eps = 1e-6)
cos = nps.inner(v_unprojected, p_ref) / nps.mag(p_ref)
cos = np.clip(cos, -1, 1)
testutils.confirm_equal( np.arccos(cos),
np.zeros((p_ref.shape[0],), dtype=float),
msg = f"Unprojecting in-place {intrinsics[0]}",
eps = 1e-6)
### unproject gradients
v_unprojected,dv_dq,dv_di = mrcal.unproject(q_projected,
*intrinsics, get_gradients=True)
# I'd like to turn this on, but unproject() doesn't behave the way it
# should, so this test always fails currently
#
# testutils.confirm_equal( v_unprojected,
# v_unprojected_nograd,
# msg = f"Unproject() should return the same thing whether get_gradients or not",
# eps = 1e-6)
if re.match('^LENSMODEL_CAHVORE',intrinsics[0]):
dv_dqi_ref = None
else:
# Two different gradient computations, to match the two different ways the
# internal computation is performed
if intrinsics[0] == 'LENSMODEL_PINHOLE' or \
intrinsics[0] == 'LENSMODEL_STEREOGRAPHIC' or \
intrinsics[0] == 'LENSMODEL_LATLON' or \
intrinsics[0] == 'LENSMODEL_LONLAT':
@nps.broadcast_define( ((2,),('N',)) )
def grad_broadcasted(q_ref, i_ref):
return grad(lambda qi: mrcal.unproject(qi[:2], intrinsics[0], qi[2:]),
nps.glue(q_ref,i_ref, axis=-1))
dv_dqi_ref = grad_broadcasted(q_projected,intrinsics[1])
else:
@nps.broadcast_define( ((2,),('N',)) )
def grad_broadcasted(q_ref, i_ref):
return grad(lambda qi: \
mrcal.unproject_stereographic( \
mrcal.project_stereographic(
mrcal.unproject(qi[:2], intrinsics[0], qi[2:]))),
nps.glue(q_ref,i_ref, axis=-1))
dv_dqi_ref = grad_broadcasted(q_projected,intrinsics[1])
testutils.confirm_equal(mrcal.project(v_unprojected, *intrinsics),
q_projected,
msg = f"Unprojecting {intrinsics[0]} with grad",
eps = 1e-2)
testutils.confirm_equal(dv_dq,
dv_dqi_ref[...,:2],
msg = f"dv_dq: {intrinsics[0]}",
worstcase = True,
relative = True,
eps = 0.01)
testutils.confirm_equal(dv_di,
dv_dqi_ref[...,2:],
msg = f"dv_di {intrinsics[0]}",
worstcase = True,
relative = True,
eps = 0.01)
# Normalized unprojected gradients
v_unprojected,dv_dq,dv_di = mrcal.unproject(q_projected,
*intrinsics,
normalize = True,
get_gradients = True)
testutils.confirm_equal( nps.norm2(v_unprojected),
1,
msg = f"Unprojected v (with gradients) are normalized",
eps = 1e-6)
cos = nps.inner(v_unprojected, p_ref) / nps.mag(p_ref)
cos = np.clip(cos, -1, 1)
testutils.confirm_equal( np.arccos(cos),
np.zeros((p_ref.shape[0],), dtype=float),
msg = f"Unprojecting (normalized, with gradients) {intrinsics[0]}",
eps = 1e-6)
if re.match('^LENSMODEL_CAHVORE',intrinsics[0]):
dvnormalized_dqi_ref = None
else:
@nps.broadcast_define( ((2,),('N',)) )
def grad_normalized_broadcasted(q_ref, i_ref):
return grad(lambda qi: \
mrcal.unproject(qi[:2], intrinsics[0], qi[2:], normalize=True),
nps.glue(q_ref,i_ref, axis=-1))
dvnormalized_dqi_ref = grad_normalized_broadcasted(q_projected,intrinsics[1])
testutils.confirm_equal(dv_dq,
dvnormalized_dqi_ref[...,:2],
msg = f"dv_dq (normalized v): {intrinsics[0]}",
worstcase = True,
relative = True,
eps = 0.01)
testutils.confirm_equal(dv_di,
dvnormalized_dqi_ref[...,2:],
msg = f"dv_di (normalized v): {intrinsics[0]}",
worstcase = True,
relative = True,
eps = 0.01)
# unproject() with gradients, in-place
if 1:
# Normalized output
out=[v_unprojected,dv_dq,dv_di]
out[0] *= 0
out[1] *= 0
out[2] *= 0
mrcal.unproject(q_projected,
*intrinsics,
normalize = True,
get_gradients = True,
out = out)
testutils.confirm_equal( nps.norm2(v_unprojected),
1,
msg = f"Unprojected v (with gradients, in-place) are normalized",
eps = 1e-6)
cos = nps.inner(v_unprojected, p_ref) / nps.mag(p_ref)
cos = np.clip(cos, -1, 1)
testutils.confirm_equal( np.arccos(cos),
np.zeros((p_ref.shape[0],), dtype=float),
msg = f"Unprojecting (normalized, with gradients, in-place) {intrinsics[0]}",
eps = 1e-6)
if dvnormalized_dqi_ref is not None:
testutils.confirm_equal(dv_dq,
dvnormalized_dqi_ref[...,:2],
msg = f"dv_dq (normalized v, in-place): {intrinsics[0]}",
worstcase = True,
relative = True,
eps = 0.01)
testutils.confirm_equal(dv_di,
dvnormalized_dqi_ref[...,2:],
msg = f"dv_di (normalized v, in-place): {intrinsics[0]}",
worstcase = True,
relative = True,
eps = 0.01)
if 1:
# un-normalized output
out=[v_unprojected,dv_dq,dv_di]
out[0] *= 0
out[1] *= 0
out[2] *= 0
mrcal.unproject(q_projected,
*intrinsics,
normalize = False,
get_gradients = True,
out = out)
cos = nps.inner(v_unprojected, p_ref) / nps.mag(p_ref)
cos = np.clip(cos, -1, 1)
testutils.confirm_equal( np.arccos(cos),
np.zeros((p_ref.shape[0],), dtype=float),
msg = f"Unprojecting (non-normalized, with gradients, in-place) {intrinsics[0]}",
eps = 1e-6)
if dv_dqi_ref is not None:
testutils.confirm_equal(dv_dq,
dv_dqi_ref[...,:2],
msg = f"dv_dq (unnormalized v, in-place): {intrinsics[0]}",
worstcase = True,
relative = True,
eps = 0.01)
testutils.confirm_equal(dv_di,
dv_dqi_ref[...,2:],
msg = f"dv_di (unnormalized v, in-place): {intrinsics[0]}",
worstcase = True,
relative = True,
eps = 0.01)
# a few points, some wide, some not. None behind the camera
p = np.array(((1.0, 2.0, 10.0),
(-1.1, 0.3, 1.0),
(-0.9, -1.5, 1.0)))
check( ('LENSMODEL_PINHOLE', np.array(((1512., 1112, 500., 333.),
(1512., 1112, 500., 433.),
(1512., 1112, 500., 533.)))),
p,
np.array([[ 651.2, 555.4],
[-1163.2, 766.6],
[ -860.8, -1135. ]]))
check( ('LENSMODEL_STEREOGRAPHIC', np.array(((1512., 1112, 500., 333.),
(1502., 1112, 500., 433.),
(1522., 1112, 500., 533.)))),
p,
np.array([[ 649.35582325, 552.6874014],
[-813.05440267, 698.1222302],
[-408.67354332, -573.48815174]]))
check( ('LENSMODEL_LATLON', np.array(((1512., 1112, 500., 333.),
(1502., 1112, 500., 433.),
(1522., 1112, 500., 533.)))),
p,
np.array([[ 647.79131656, 552.50386255],
[-718.86844854, 757.09995546],
[-204.73403533, -559.86662025]]))
check( ('LENSMODEL_LONLAT', np.array(((1512., 1112, 500., 333.),
(1502., 1112, 500., 433.),
(1522., 1112, 500., 533.)))),
p,
np.array([[ 650.69900257, 551.44238248],
[-751.13786254, 654.42977413],
[-615.34458492, -400.73749463]]))
check( ('LENSMODEL_OPENCV4', np.array((1512., 1112, 500., 333.,
-0.012, 0.035, -0.001, 0.002))),
p,
np.array([[ 651.27371 , 555.23042 ],
[-1223.38516 , 678.01468 ],
[-1246.7310448, -1822.799928 ]]))
check( ('LENSMODEL_OPENCV5', np.array((1512., 1112, 500., 333.,
-0.012, 0.035, -0.001, 0.002, 0.019))),
p,
np.array([[ 651.2740691 , 555.2309482 ],
[-1292.8121176 , 691.9401448 ],
[-1987.550162 , -2730.85863427]]))
check( ('LENSMODEL_OPENCV8', np.array((1512., 1112, 500., 333.,
-0.012, 0.035, -0.001, 0.002, 0.019, 0.014, -0.056, 0.050))),
p,
np.array([[ 651.1885442 , 555.10514968],
[-1234.45480366, 680.23499814],
[ -770.03274263, -1238.4871943 ]]))
check( ('LENSMODEL_CAHVOR', np.array((4842.918, 4842.771, 1970.528, 1085.302,
-0.001, 0.002, -0.637, -0.002, 0.016))),
p,
np.array([[ 2143.17840406, 1442.93419919],
[ -92.63813066, 1653.09646897],
[ -249.83199315, -2606.46477164]]))
# E ~ 0: almost central
check( ('LENSMODEL_CAHVORE_linearity=0.00',
np.array((4842.918, 4842.771, 1970.528, 1085.302,
-0.001, 0.002, -0.637, -0.002, 0.016, 1e-8, 2e-8, 3e-8))),
p,
np.array(([2140.340769278752759, 1437.371480086463635],
[496.634661939782575, 1493.316705796434917],
[970.117888562484495, -568.301135889864668])))
# E != 0: noncentral
check( ('LENSMODEL_CAHVORE_linearity=0.00',
np.array((4842.918, 4842.771, 1970.528, 1085.302,
-0.001, 0.002, -0.637, -0.002, 0.016, 1e-2, 2e-2, 3e-2))),
p,
np.array(([2140.342263050081783, 1437.374408380910836],
[491.682975341940221, 1494.659342555658441],
[962.730552352575160, -580.643118338666000])))
# E != 0: noncentral. AND linearity > 0
check( ('LENSMODEL_CAHVORE_linearity=0.40',
np.array((4842.918, 4842.771, 1970.528, 1085.302,
-0.001, 0.002, -0.637, -0.002, 0.016, 1e-2, 2e-2, 3e-2))),
p,
np.array(([2140.788976358770469, 1438.250116781426641],
[426.278593220184689, 1512.393568241352796],
[882.926242407330619, -713.971745152981612])))
# Note that some of the projected points are behind the camera (z<0), which is
# possible with these models. Also note that some of the projected points are
# off the imager (x<0). This is aphysical, but it just means that the model was
# made up; which it was. The math still works normally, and this is just fine as
# a test
check( ('LENSMODEL_SPLINED_STEREOGRAPHIC_order=3_Nx=11_Ny=8_fov_x_deg=200',
np.array([ 1500.0, 1800.0, 1499.5,999.5,
2.017284705,1.242204557,2.053514381,1.214368063,2.0379067,1.212609628,
2.033278227,1.183689487,2.040018023,1.188554431,2.069146825,1.196304649,
2.085708658,1.186478238,2.065787617,1.163377825,2.086372192,1.138856716,
2.131609155,1.125678279,2.128812604,1.120525061,2.00841491,1.21864154,
2.024522768,1.239588759,2.034947935,1.19814079,2.065474055,1.19897294,
2.044562395,1.200557321,2.087714092,1.160440038,2.086478691,1.151822407,
2.112862582,1.147567288,2.101575718,1.146312256,2.10056469,1.157015327,
2.113488262,1.111679758,2.019837901,1.244168216,2.025847768,1.215633807,
2.041980956,1.205751212,2.075077056,1.199787561,2.070877831,1.203261678,
2.067244278,1.184705736,2.082225077,1.185558149,2.091519961,1.17501817,
2.120258866,1.137775228,2.120020747,1.152409316,2.121870228,1.113069319,
2.043650555,1.247757041,2.019661062,1.230723629,2.067917203,1.209753396,
2.035034141,1.219514335,2.045350268,1.178474255,2.046346049,1.169372592,
2.097839998,1.194836758,2.112724938,1.172186377,2.110996386,1.154899043,
2.128456883,1.133228404,2.122513384,1.131717886,2.044279196,1.233288366,
2.023197297,1.230118703,2.06707694,1.199998862,2.044147271,1.191607451,
2.058590053,1.1677808,2.081593501,1.182074581,2.08663053,1.159156329,
2.084329086,1.157727374,2.073666528,1.151261965,2.114290905,1.144710519,
2.138600912,1.119405248,2.016299528,1.206147494,2.029434175,1.211507857,
2.057936091,1.19801196,2.035691392,1.174035359,2.084718618,1.203604729,
2.085910021,1.158385222,2.080800068,1.150199852,2.087991586,1.162019581,
2.094754507,1.151061493,2.115144642,1.154299799,2.107014195,1.127608146,
2.005632475,1.238607328,2.02033157,1.202101384,2.061021703,1.214868271,
2.043015135,1.211903685,2.05291186,1.188092787,2.09486724,1.179277314,
2.078230124,1.186273023,2.077743945,1.148028845,2.081634186,1.131207467,
2.112936851,1.126412871,2.113220553,1.114991063,2.017901873,1.244588667,
2.051238803,1.201855728,2.043256406,1.216674722,2.035286046,1.178380907,
2.08028318,1.178783085,2.051214271,1.173560417,2.059298121,1.182414688,
2.094607679,1.177960959,2.086998287,1.147371259,2.12029442,1.138197348,
2.138994213, 1.114846113,],)),
# some points behind the camera!
np.array([[-0.8479983, -0.52999894, -0.34690877],
[-0.93984618, 0.34159794, -0.16119387],
[-0.97738792, 0.21145412, 5.49068928]]),
np.array([[ 965.9173441 , 524.31894367],
[1246.58668369, 4621.35427783],
[4329.41598149, 3183.75121559]]))
check( ('LENSMODEL_SPLINED_STEREOGRAPHIC_order=2_Nx=11_Ny=8_fov_x_deg=200',
np.array([ 1500.0, 1800.0, 1499.5,999.5,
2.017284705,1.242204557,2.053514381,1.214368063,2.0379067,1.212609628,
2.033278227,1.183689487,2.040018023,1.188554431,2.069146825,1.196304649,
2.085708658,1.186478238,2.065787617,1.163377825,2.086372192,1.138856716,
2.131609155,1.125678279,2.128812604,1.120525061,2.00841491,1.21864154,
2.024522768,1.239588759,2.034947935,1.19814079,2.065474055,1.19897294,
2.044562395,1.200557321,2.087714092,1.160440038,2.086478691,1.151822407,
2.112862582,1.147567288,2.101575718,1.146312256,2.10056469,1.157015327,
2.113488262,1.111679758,2.019837901,1.244168216,2.025847768,1.215633807,
2.041980956,1.205751212,2.075077056,1.199787561,2.070877831,1.203261678,
2.067244278,1.184705736,2.082225077,1.185558149,2.091519961,1.17501817,
2.120258866,1.137775228,2.120020747,1.152409316,2.121870228,1.113069319,
2.043650555,1.247757041,2.019661062,1.230723629,2.067917203,1.209753396,
2.035034141,1.219514335,2.045350268,1.178474255,2.046346049,1.169372592,
2.097839998,1.194836758,2.112724938,1.172186377,2.110996386,1.154899043,
2.128456883,1.133228404,2.122513384,1.131717886,2.044279196,1.233288366,
2.023197297,1.230118703,2.06707694,1.199998862,2.044147271,1.191607451,
2.058590053,1.1677808,2.081593501,1.182074581,2.08663053,1.159156329,
2.084329086,1.157727374,2.073666528,1.151261965,2.114290905,1.144710519,
2.138600912,1.119405248,2.016299528,1.206147494,2.029434175,1.211507857,
2.057936091,1.19801196,2.035691392,1.174035359,2.084718618,1.203604729,
2.085910021,1.158385222,2.080800068,1.150199852,2.087991586,1.162019581,
2.094754507,1.151061493,2.115144642,1.154299799,2.107014195,1.127608146,
2.005632475,1.238607328,2.02033157,1.202101384,2.061021703,1.214868271,
2.043015135,1.211903685,2.05291186,1.188092787,2.09486724,1.179277314,
2.078230124,1.186273023,2.077743945,1.148028845,2.081634186,1.131207467,
2.112936851,1.126412871,2.113220553,1.114991063,2.017901873,1.244588667,
2.051238803,1.201855728,2.043256406,1.216674722,2.035286046,1.178380907,
2.08028318,1.178783085,2.051214271,1.173560417,2.059298121,1.182414688,
2.094607679,1.177960959,2.086998287,1.147371259,2.12029442,1.138197348,
2.138994213, 1.114846113,],)),
# some points behind the camera!
np.array([[-0.8479983, -0.52999894, -0.34690877],
[-0.93984618, 0.34159794, -0.16119387],
[-0.97738792, 0.21145412, 5.49068928]]),
np.array([[ 958.48347896, 529.99410342],
[1229.87308989, 4625.05434521],
[4327.8166836 , 3183.44237796]]))
testutils.finish()
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