1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
|
#!/usr/bin/env python3
r'''Tests the stereo routines
'''
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 scipy.interpolate
import testutils
model0 = mrcal.cameramodel(f"{testdir}/data/cam0.opencv8.cameramodel")
model1 = mrcal.cameramodel(model0)
for lensmodel in ('LENSMODEL_LATLON', 'LENSMODEL_PINHOLE'):
# I create geometries to test. First off, a vanilla geometry for left-right stereo
rt01 = np.array((0,0,0, 3.0, 0, 0))
model1.rt_ref_cam( mrcal.compose_rt(model0.rt_ref_cam(),
rt01))
az_fov_deg = 90
el_fov_deg = 50
models_rectified = \
mrcal.rectified_system( (model0, model1),
az_fov_deg = az_fov_deg,
el_fov_deg = el_fov_deg,
pixels_per_deg_az = -1./8.,
pixels_per_deg_el = -1./4.,
rectification_model = lensmodel)
az0 = 0.
el0 = 0.
try:
mrcal.stereo._validate_models_rectified(models_rectified)
testutils.confirm(True,
msg=f'Generated models pass validation ({lensmodel})')
except:
testutils.confirm(False,
msg=f'Generated models pass validation ({lensmodel})')
Rt_cam0_rect = mrcal.compose_Rt( model0.Rt_cam_ref(),
models_rectified[0].Rt_ref_cam())
Rt01_rectified = mrcal.compose_Rt( models_rectified[0].Rt_cam_ref(),
models_rectified[1].Rt_ref_cam())
testutils.confirm_equal(models_rectified[0].intrinsics()[0], lensmodel,
msg=f'model0 has the right lensmodel ({lensmodel})')
testutils.confirm_equal(models_rectified[1].intrinsics()[0], lensmodel,
msg=f'model1 has the right lensmodel ({lensmodel})')
testutils.confirm_equal(Rt_cam0_rect, mrcal.identity_Rt(),
msg=f'vanilla stereo has a vanilla geometry ({lensmodel})')
testutils.confirm_equal( Rt01_rectified[3,0],
nps.mag(rt01[3:]),
msg=f'vanilla stereo: baseline ({lensmodel})')
Naz,Nel = models_rectified[0].imagersize()
q0 = np.array(((Naz-1.)/2., (Nel-1.)/2.))
v0 = mrcal.unproject(q0, *models_rectified[0].intrinsics(), normalize=True)
if lensmodel == 'LENSMODEL_LATLON':
v0_rect = mrcal.unproject_latlon(np.array((az0, el0)))
# already normalized
testutils.confirm_equal( v0_rect, v0,
msg=f'vanilla stereo: az0,el0 represent the same point ({lensmodel})')
else:
v0_rect = mrcal.unproject_pinhole(np.array((np.tan(az0), np.tan(el0))))
v0_rect /= nps.mag(v0_rect)
testutils.confirm_equal( v0_rect, v0,
msg=f'vanilla stereo: az0,el0 represent the same point ({lensmodel})',
eps = 1e-3)
dq0x = np.array((1e-1, 0))
dq0y = np.array((0, 1e-1))
v0x = mrcal.unproject(q0+dq0x, *models_rectified[0].intrinsics())
v0y = mrcal.unproject(q0+dq0y, *models_rectified[0].intrinsics())
dthx = np.arccos(nps.inner(v0x,v0)/np.sqrt(nps.norm2(v0x)*nps.norm2(v0)))
dthy = np.arccos(nps.inner(v0y,v0)/np.sqrt(nps.norm2(v0y)*nps.norm2(v0)))
pixels_per_rad_az_rect = nps.mag(dq0x)/dthx
pixels_per_rad_el_rect = nps.mag(dq0y)/dthy
q0_cam0 = mrcal.project(mrcal.rotate_point_R(Rt_cam0_rect[:3,:], v0),
*model0.intrinsics())
q0x_cam0 = mrcal.project(mrcal.rotate_point_R(Rt_cam0_rect[:3,:], v0x),
*model0.intrinsics())
q0y_cam0 = mrcal.project(mrcal.rotate_point_R(Rt_cam0_rect[:3,:], v0y),
*model0.intrinsics())
pixels_per_rad_az_cam0 = nps.mag(q0x_cam0 - q0_cam0)/dthx
pixels_per_rad_el_cam0 = nps.mag(q0y_cam0 - q0_cam0)/dthy
testutils.confirm_equal(pixels_per_rad_az_rect * 8.,
pixels_per_rad_az_cam0,
msg=f'vanilla stereo: az pixel density ({lensmodel})',
relative = True,
eps = 1e-2)
testutils.confirm_equal(pixels_per_rad_el_rect * 4.,
pixels_per_rad_el_cam0,
msg=f'vanilla stereo: el pixel density ({lensmodel})',
relative = True,
eps = 1e-2)
v0 = mrcal.unproject(np.array((0, (Nel-1.)/2.)), *models_rectified[0].intrinsics())
v1 = mrcal.unproject(np.array((Naz-1,(Nel-1.)/2.)), *models_rectified[0].intrinsics())
az_fov_deg_observed = np.arccos(nps.inner(v0,v1)/(nps.mag(v0)*nps.mag(v1))) * 180./np.pi
testutils.confirm_equal(az_fov_deg_observed,
az_fov_deg,
msg=f'vanilla stereo: az_fov ({lensmodel})',
eps = 0.5)
v0 = mrcal.unproject(np.array(((Naz-1.)/2., 0, )), *models_rectified[0].intrinsics())
v0[0] = 0 # el_fov applies at the stereo center only
v1 = mrcal.unproject(np.array(((Naz-1.)/2., Nel-1,)), *models_rectified[0].intrinsics())
v1[0] = 0
el_fov_deg_observed = np.arccos(nps.inner(v0,v1)/(nps.mag(v0)*nps.mag(v1))) * 180./np.pi
testutils.confirm_equal(el_fov_deg_observed,
el_fov_deg,
msg=f'vanilla stereo: el_fov ({lensmodel})',
eps = 0.5)
############### Complex geometry
# Left-right stereo, with sizeable rotation and position fuzz.
# I especially make sure there's a forward/back shift
rt01 = np.array((0.1, 0.2, 0.05, 3.0, 0.2, 1.0))
model1.rt_ref_cam( mrcal.compose_rt(model0.rt_ref_cam(),
rt01))
el0_deg = 10.0
models_rectified = \
mrcal.rectified_system( (model0, model1),
az_fov_deg = az_fov_deg,
el_fov_deg = el_fov_deg,
el0_deg = el0_deg,
pixels_per_deg_az = -1./8.,
pixels_per_deg_el = -1./4.,
rectification_model = lensmodel)
el0 = el0_deg*np.pi/180.
# az0 is the "forward" direction of the two cameras, relative to the
# baseline vector
baseline = rt01[3:] / nps.mag(rt01[3:])
# "forward" for each of the two cameras, in the cam0 coord system
forward0 = np.array((0,0,1.))
forward1 = mrcal.rotate_point_r(rt01[:3], np.array((0,0,1.)))
forward01 = forward0 + forward1
az0 = np.arcsin( nps.inner(forward01,baseline) / nps.mag(forward01) )
try:
mrcal.stereo._validate_models_rectified(models_rectified)
testutils.confirm(True,
msg=f'Generated models pass validation ({lensmodel})')
except:
testutils.confirm(False,
msg=f'Generated models pass validation ({lensmodel})')
Rt_cam0_rect = mrcal.compose_Rt( model0.Rt_cam_ref(),
models_rectified[0].Rt_ref_cam())
Rt01_rectified = mrcal.compose_Rt( models_rectified[0].Rt_cam_ref(),
models_rectified[1].Rt_ref_cam())
# I visualized the geometry, and confirmed that it is correct. The below array
# is the correct-looking geometry
#
# Rt_cam0_ref = model0.Rt_cam_ref()
# Rt_rect_ref = mrcal.compose_Rt( mrcal.invert_Rt(Rt_cam0_rect),
# Rt_cam0_ref )
# rt_rect_ref = mrcal.rt_from_Rt(Rt_rect_ref)
# mrcal.show_geometry( [ model0, model1, rt_rect_ref ],
# cameranames = ( "camera0", "camera1", "stereo" ),
# show_calobjects = False,
# wait = True )
# print(repr(Rt_cam0_rect))
testutils.confirm_equal(Rt_cam0_rect,
np.array([[ 0.9467916 , -0.08500675, -0.31041828],
[ 0.06311944, 0.99480206, -0.07990489],
[ 0.3155972 , 0.05605985, 0.94723582],
[ 0. , -0. , -0. ]]),
msg=f'complex stereo geometry ({lensmodel})')
testutils.confirm_equal( Rt01_rectified[3,0],
nps.mag(rt01[3:]),
msg=f'complex stereo: baseline ({lensmodel})')
Naz,Nel = models_rectified[0].imagersize()
q0 = np.array(((Naz-1.)/2., (Nel-1.)/2.))
v0 = mrcal.unproject(q0, *models_rectified[0].intrinsics(), normalize=True)
if lensmodel == 'LENSMODEL_LATLON':
v0_rect = mrcal.unproject_latlon(np.array((az0, el0)))
# already normalized
testutils.confirm_equal( v0_rect, v0,
msg=f'complex stereo: az0,el0 represent the same point ({lensmodel})')
else:
v0_rect = mrcal.unproject_pinhole(np.array((np.tan(az0), np.tan(el0))))
v0_rect /= nps.mag(v0_rect)
testutils.confirm_equal( v0_rect, v0,
msg=f'complex stereo: az0,el0 represent the same point ({lensmodel})',
eps = 1e-3)
dq0x = np.array((1e-1, 0))
dq0y = np.array((0, 1e-1))
v0x = mrcal.unproject(q0+dq0x, *models_rectified[0].intrinsics())
v0y = mrcal.unproject(q0+dq0y, *models_rectified[0].intrinsics())
dthx = np.arccos(nps.inner(v0x,v0)/np.sqrt(nps.norm2(v0x)*nps.norm2(v0)))
dthy = np.arccos(nps.inner(v0y,v0)/np.sqrt(nps.norm2(v0y)*nps.norm2(v0)))
pixels_per_rad_az_rect = nps.mag(dq0x)/dthx
pixels_per_rad_el_rect = nps.mag(dq0y)/dthy
q0_cam0 = mrcal.project(mrcal.rotate_point_R(Rt_cam0_rect[:3,:], v0),
*model0.intrinsics())
q0x_cam0 = mrcal.project(mrcal.rotate_point_R(Rt_cam0_rect[:3,:], v0x),
*model0.intrinsics())
q0y_cam0 = mrcal.project(mrcal.rotate_point_R(Rt_cam0_rect[:3,:], v0y),
*model0.intrinsics())
pixels_per_rad_az_cam0 = nps.mag(q0x_cam0 - q0_cam0)/dthx
pixels_per_rad_el_cam0 = nps.mag(q0y_cam0 - q0_cam0)/dthy
testutils.confirm_equal(pixels_per_rad_az_rect * 8.,
pixels_per_rad_az_cam0,
msg=f'complex stereo: az pixel density ({lensmodel})',
relative = True,
eps = 1e-2)
testutils.confirm_equal(pixels_per_rad_el_rect * 4.,
pixels_per_rad_el_cam0,
msg=f'complex stereo: el pixel density ({lensmodel})',
relative = True,
eps = 1e-2)
v0 = mrcal.unproject(np.array((0, (Nel-1.)/2.)), *models_rectified[0].intrinsics())
v1 = mrcal.unproject(np.array((Naz-1,(Nel-1.)/2.)), *models_rectified[0].intrinsics())
az_fov_deg_observed = np.arccos(nps.inner(v0,v1)/(nps.mag(v0)*nps.mag(v1))) * 180./np.pi
testutils.confirm_equal(az_fov_deg_observed,
az_fov_deg,
msg=f'complex stereo: az_fov ({lensmodel})',
eps = 1.)
v0 = mrcal.unproject(np.array(((Naz-1.)/2., 0, )), *models_rectified[0].intrinsics())
v0[0] = 0 # el_fov applies at the stereo center only
v1 = mrcal.unproject(np.array(((Naz-1.)/2., Nel-1,)), *models_rectified[0].intrinsics())
v1[0] = 0
el_fov_deg_observed = np.arccos(nps.inner(v0,v1)/(nps.mag(v0)*nps.mag(v1))) * 180./np.pi
testutils.confirm_equal(el_fov_deg_observed,
el_fov_deg,
msg=f'complex stereo: el_fov ({lensmodel})',
eps = 0.5)
# I examine points somewhere in space. I make sure the rectification maps
# transform it properly. And I compute what its az,el and disparity would have
# been, and I check the geometric functions
pcam0 = np.array((( 1., 2., 12.),
(-4., 3., 12.)))
qcam0 = mrcal.project( pcam0, *model0.intrinsics() )
pcam1 = mrcal.transform_point_rt(mrcal.invert_rt(rt01), pcam0)
qcam1 = mrcal.project( pcam1, *model1.intrinsics() )
prect0 = mrcal.transform_point_Rt( mrcal.invert_Rt(Rt_cam0_rect), pcam0)
prect1 = prect0 - Rt01_rectified[3,:]
qrect0 = mrcal.project(prect0, *models_rectified[0].intrinsics())
qrect1 = mrcal.project(prect1, *models_rectified[1].intrinsics())
Naz,Nel = models_rectified[0].imagersize()
row = np.arange(Naz, dtype=float)
col = np.arange(Nel, dtype=float)
rectification_maps = mrcal.rectification_maps((model0,model1),
models_rectified)
interp_rectification_map0x = \
scipy.interpolate.RectBivariateSpline(row, col,
nps.transpose(rectification_maps[0][...,0]))
interp_rectification_map0y = \
scipy.interpolate.RectBivariateSpline(row, col,
nps.transpose(rectification_maps[0][...,1]))
interp_rectification_map1x = \
scipy.interpolate.RectBivariateSpline(row, col,
nps.transpose(rectification_maps[1][...,0]))
interp_rectification_map1y = \
scipy.interpolate.RectBivariateSpline(row, col,
nps.transpose(rectification_maps[1][...,1]))
if lensmodel == 'LENSMODEL_LATLON':
qcam0_from_map = \
nps.transpose( nps.cat( interp_rectification_map0x(*nps.transpose(qrect0), grid=False),
interp_rectification_map0y(*nps.transpose(qrect0), grid=False) ) )
qcam1_from_map = \
nps.transpose( nps.cat( interp_rectification_map1x(*nps.transpose(qrect1), grid=False),
interp_rectification_map1y(*nps.transpose(qrect1), grid=False) ) )
else:
qcam0_from_map = \
nps.transpose( nps.cat( interp_rectification_map0x(*nps.transpose(qrect0), grid=False),
interp_rectification_map0y(*nps.transpose(qrect0), grid=False) ) )
qcam1_from_map = \
nps.transpose( nps.cat( interp_rectification_map1x(*nps.transpose(qrect1), grid=False),
interp_rectification_map1y(*nps.transpose(qrect1), grid=False) ) )
testutils.confirm_equal( qcam0_from_map, qcam0,
eps=1e-1,
msg=f'rectification map for camera 0 points ({lensmodel})')
testutils.confirm_equal( qcam1_from_map, qcam1,
eps=1e-1,
msg=f'rectification map for camera 1 points ({lensmodel})')
# same point, so we should have the same el
testutils.confirm_equal( qrect0[:,1], qrect1[:,1],
msg=f'elevations of the same observed point match ({lensmodel})')
disparity = qrect0[:,0] - qrect1[:,0]
r = mrcal.stereo_range( disparity,
models_rectified,
qrect0 = qrect0,
)
testutils.confirm_equal( r, nps.mag(pcam0),
msg=f'stereo_range reports the right thing ({lensmodel})')
r = mrcal.stereo_range( disparity[0],
models_rectified,
qrect0 = qrect0[0],
)
testutils.confirm_equal( r, nps.mag(pcam0[0]),
msg=f'stereo_range (1-element array) reports the right thing ({lensmodel})',
eps=2e-6)
r = mrcal.stereo_range( float(disparity[0]),
models_rectified,
qrect0 = qrect0[0],
)
testutils.confirm_equal( r, float(nps.mag(pcam0[0])),
msg=f'stereo_range (scalar) reports the right thing ({lensmodel})',
eps=2e-6)
r = mrcal.stereo_range( np.int16(-1),
models_rectified,
qrect0 = qrect0[0],
disparity_min = 0)
testutils.confirm_equal( r, 0,
msg=f'stereo_range sparse processing handles invalid disparity correctly ({lensmodel})')
# dense range
#
# I make up a disparity, and make sure that reasonable ranges come back. I
# also make sure that we correctly handle invalid disparity values
disparity_scale = 16
disparity_mean = np.mean(disparity)
range_mean = np.mean(nps.mag(pcam0))
disparity_dense = np.int16(disparity_scale * disparity_mean) * np.ones( (Nel,Naz), dtype=np.int16 )
# add some invalid pixels
disparity_dense[0,0] = -1
mask_valid = disparity_dense > 0
r = mrcal.stereo_range( disparity_dense,
models_rectified,
disparity_scale = disparity_scale,
disparity_min = 0)
r_valid = r[mask_valid]
testutils.confirm( np.all(r_valid > range_mean/2) and np.all(r_valid < range_mean*2. ),
msg=f'stereo_range dense processing produces reasonable values ({lensmodel})')
testutils.confirm_equal( r[0,0], 0,
eps = 1e-6,
msg=f'stereo_range dense processing handles invalid disparity correctly ({lensmodel})')
disparity = qrect0[:,0] - qrect1[:,0]
p = mrcal.stereo_unproject( disparity,
models_rectified,
qrect0 = qrect0,
)
testutils.confirm_equal( p, prect0,
msg=f'stereo_unproject reports the right thing ({lensmodel})')
p = mrcal.stereo_unproject( float(disparity[0]),
models_rectified,
qrect0 = qrect0[0],
)
testutils.confirm_equal( p, prect0[0],
msg=f'stereo_unproject (scalar) reports the right thing ({lensmodel})')
testutils.finish()
|
