1 // Copyright 2003, 2004, 2005 David Hilvert <dhilvert@auricle.dyndns.org>,
2 // <dhilvert@ugcs.caltech.edu>
4 /* This file is part of the Anti-Lamenessing Engine.
6 The Anti-Lamenessing Engine is free software; you can redistribute it and/or modify
7 it under the terms of the GNU General Public License as published by
8 the Free Software Foundation; either version 2 of the License, or
9 (at your option) any later version.
11 The Anti-Lamenessing Engine is distributed in the hope that it will be useful,
12 but WITHOUT ANY WARRANTY; without even the implied warranty of
13 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14 GNU General Public License for more details.
16 You should have received a copy of the GNU General Public License
17 along with the Anti-Lamenessing Engine; if not, write to the Free Software
18 Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
22 * d3/scene.h: Representation of a 3D scene.
31 * View angle multiplier.
33 * Setting this to a value larger than one can be useful for debugging.
36 #define VIEW_ANGLE_MULTIPLIER 1
43 static ale_pos front_clip
;
44 static ale_pos rear_clip
;
47 * Decimation exponents for geometry
49 static int primary_decimation_upper
;
50 static int input_decimation_lower
;
51 static int output_decimation_preferred
;
56 static int output_clip
;
61 static const char *load_model_name
;
62 static const char *save_model_name
;
65 * Occupancy attenuation
68 static double occ_att
;
71 * Normalization of output by weight
74 static int normalize_weights
;
80 static double falloff_exponent
;
83 * Third-camera error multiplier
85 static double tc_multiplier
;
88 * Occupancy update iterations
90 static unsigned int ou_iterations
;
95 static unsigned int pairwise_ambiguity
;
98 * Pairwise comparisons
100 static const char *pairwise_comparisons
;
105 static int d3px_count
;
106 static double *d3px_parameters
;
111 static const ale_real nearness
;
114 * Encounter threshold for defined pixels.
116 static double encounter_threshold
;
119 * Structure to hold input frame information at levels of
120 * detail between full detail and full decimation.
124 unsigned int entries
;
125 std::vector
<const d2::image
*> im
;
126 std::vector
<pt
> transformation
;
132 lod_image(unsigned int _f
) {
137 im
.push_back(d2::image_rw::copy(f
, "3D reference image"));
140 _pt
= d3::align::projective(f
);
141 _pt
.scale(1 / _pt
.scale_2d());
142 transformation
.push_back(_pt
);
144 while (im
.back()->height() > 4
145 && im
.back()->width() > 4) {
147 im
.push_back(im
.back()->scale_by_half("3D, reduced LOD"));
150 _pt
.scale(1 / _pt
.scale_2d() / pow(2, entries
));
151 transformation
.push_back(_pt
);
158 * Get the number of scales
160 unsigned int count() {
167 const d2::image
*get_image(unsigned int i
) {
173 int in_bounds(d2::point p
) {
174 return im
[0]->in_bounds(p
);
178 * Get a 'trilinear' color. We currently don't do interpolation
179 * between levels of detail; hence, it's discontinuous in tl_coord.
181 d2::pixel
get_tl(d2::point p
, ale_pos tl_coord
) {
183 assert(in_bounds(p
));
185 tl_coord
= round(tl_coord
);
187 if (tl_coord
>= entries
)
192 p
= p
/ (ale_pos
) pow(2, tl_coord
);
194 unsigned int itlc
= (unsigned int) tl_coord
;
196 if (p
[0] > im
[itlc
]->height() - 1)
197 p
[0] = im
[itlc
]->height() - 1;
198 if (p
[1] > im
[itlc
]->width() - 1)
199 p
[1] = im
[itlc
]->width() - 1;
204 return im
[itlc
]->get_bl(p
);
207 d2::pixel
get_max_diff(d2::point p
, ale_pos tl_coord
) {
208 assert(in_bounds(p
));
210 tl_coord
= round(tl_coord
);
212 if (tl_coord
>= entries
)
217 p
= p
/ (ale_pos
) pow(2, tl_coord
);
219 unsigned int itlc
= (unsigned int) tl_coord
;
221 if (p
[0] > im
[itlc
]->height() - 1)
222 p
[0] = im
[itlc
]->height() - 1;
223 if (p
[1] > im
[itlc
]->width() - 1)
224 p
[1] = im
[itlc
]->width() - 1;
229 return im
[itlc
]->get_max_diff(p
);
233 * Get the transformation
235 pt
get_t(unsigned int i
) {
238 return transformation
[i
];
242 * Get the camera origin in world coordinates
245 return transformation
[0].origin();
252 for (unsigned int i
= 0; i
< entries
; i
++) {
259 * Structure to hold weight information for reference images.
263 std::vector
<d2::image
*> weights
;
269 void set_image(d2::image
*im
, ale_real value
) {
270 for (unsigned int i
= 0; i
< im
->height(); i
++)
271 for (unsigned int j
= 0; i
< im
->width(); j
++)
272 im
->pix(i
, j
) = d2::pixel(value
, value
, value
);
275 d2::image
*make_image(ale_pos sf
, ale_real init_value
= 0) {
276 d2::image
*result
= new d2::image_ale_real(
277 (unsigned int) ceil(transformation
.unscaled_height() * sf
),
278 (unsigned int) ceil(transformation
.unscaled_width() * sf
), 3);
281 set_image(result
, init_value
);
289 * Explicit weight subtree
293 subtree
*children
[2][2];
295 subtree(ale_real nv
, subtree
*a
, subtree
*b
, subtree
*c
, subtree
*d
) {
304 for (int i
= 0; i
< 2; i
++)
305 for (int j
= 0; j
< 2; j
++)
306 delete children
[i
][j
];
313 ref_weights(unsigned int _f
) {
315 transformation
= d3::align::projective(f
);
320 * Check spatial bounds.
322 int in_spatial_bounds(d2::point p
) {
327 if (p
[0] > transformation
.unscaled_height() - 1)
329 if (p
[1] > transformation
.unscaled_width() - 1)
337 int in_spatial_bounds(const space::traverse
&t
) {
338 d2::point p
= transformation
.wp_unscaled(t
.get_centroid()).xy();
339 return in_spatial_bounds(p
);
343 * Increase resolution to the given level.
345 void increase_resolution(int tc
, unsigned int i
, unsigned int j
) {
346 d2::image
*im
= weights
[tc
- tc_low
];
348 assert(i
<= im
->height() - 1);
349 assert(j
<= im
->width() - 1);
352 * Check for the cases known to have no lower level of detail.
355 if (im
->pix(i
, j
)[0] == -1)
361 increase_resolution(tc
+ 1, i
/ 2, j
/ 2);
364 * Load the lower-level image structure.
367 d2::image
*iim
= weights
[tc
+ 1 - tc_low
];
370 assert(i
/ 2 <= iim
->height() - 1);
371 assert(j
/ 2 <= iim
->width() - 1);
374 * Check for the case where no lower level of detail is
378 if (iim
->pix(i
/ 2, j
/ 2)[0] == -1)
382 * Spread out the lower level of detail among (uninitialized)
386 for (unsigned int ii
= (i
/ 2) * 2; ii
< (i
/ 2) * 2 + 1; ii
++)
387 for (unsigned int jj
= (j
/ 2) * 2; jj
< (j
/ 2) * 2 + 1; jj
++) {
388 assert(ii
<= im
->height() - 1);
389 assert(jj
<= im
->width() - 1);
390 assert(im
->pix(ii
, jj
)[0] == 0);
392 im
->pix(ii
, jj
) = iim
->pix(i
/ 2, j
/ 2);
396 * Set the lower level of detail to point here.
399 iim
->pix(i
/ 2, j
/ 2)[0] = -1;
403 * Add weights to positive higher-resolution pixels where
404 * found when their current values match the given subtree
405 * values; set negative pixels to zero and return 0 if no
406 * positive higher-resolution pixels are found.
408 int add_partial(int tc
, unsigned int i
, unsigned int j
, ale_real weight
, subtree
*st
) {
409 d2::image
*im
= weights
[tc
- tc_low
];
411 assert(i
<= im
->height() - 1);
412 assert(j
<= im
->width() - 1);
415 * Check for positive values.
418 if (im
->pix(i
, j
)[0] > 0) {
419 if (st
&& st
->node_value
== im
->pix(i
, j
)[0])
420 im
->pix(i
, j
)[0] += weight
;
425 * Handle the case where there are no higher levels of detail.
429 assert(im
->pix(i
, j
)[0] == 0);
434 * Handle the case where higher levels of detail are available.
439 for (int ii
= 0; ii
< 2; ii
++)
440 for (int jj
= 0; jj
< 2; jj
++)
441 success
[ii
][jj
] = add_partial(tc
- 1, i
* 2 + ii
, j
* 2 + jj
, weight
,
442 st
? st
->children
[ii
][jj
] : NULL
);
448 im
->pix(i
, j
)[0] = 0;
452 d2::image
*iim
= weights
[tc
- 1 - tc_low
];
455 for (int ii
= 0; ii
< 2; ii
++)
456 for (int jj
= 0; jj
< 2; jj
++)
457 if (success
[ii
][jj
] == 0) {
458 assert(i
* 2 + ii
< iim
->height());
459 assert(j
* 2 + jj
< iim
->width());
461 im
->pix(i
* 2 + ii
, j
* 2 + jj
)[0] = weight
;
464 im
->pix(i
, j
)[0] = -1;
470 void add_weight(int tc
, unsigned int i
, unsigned int j
, ale_real weight
, subtree
*st
) {
471 d2::image
*im
= weights
[tc
- tc_low
];
473 assert(i
<= im
->height() - 1);
474 assert(j
<= im
->width() - 1);
477 * Increase resolution, if necessary
480 increase_resolution(tc
, i
, j
);
483 * Attempt to add the weight at levels of detail
484 * where weight is defined.
487 if (add_partial(tc
, i
, j
, weight
, st
))
491 * If no weights are defined at any level of detail,
492 * then set the weight here.
495 im
->pix(i
, j
)[0] = weight
;
498 void add_weight(int tc
, d2::point p
, ale_real weight
, subtree
*st
) {
502 unsigned int i
= (unsigned int) floor(p
[0]);
503 unsigned int j
= (unsigned int) floor(p
[1]);
505 add_weight(tc
, i
, j
, weight
, st
);
508 void add_weight(const space::traverse
&t
, ale_real weight
, subtree
*st
) {
513 ale_pos tc
= transformation
.trilinear_coordinate(t
);
514 d2::point p
= transformation
.wp_unscaled(t
.get_centroid()).xy();
515 assert(in_spatial_bounds(p
));
520 * Establish a reasonable (?) upper bound on resolution.
523 if (tc
< input_decimation_lower
) {
524 weight
/= pow(4, (input_decimation_lower
- tc
));
525 tc
= input_decimation_lower
;
529 * Initialize, if necessary.
533 tc_low
= tc_high
= (int) tc
;
535 ale_real sf
= pow(2, -tc
);
537 weights
[0] = make_image(sf
);
541 * Check resolution bounds
544 assert (tc_low
<= tc_high
);
547 * Generate additional levels of detail, if necessary.
550 while (tc
< tc_low
) {
553 ale_real sf
= pow(2, -tc_low
);
555 weights
.insert(weights
.begin(), make_image(sf
));
558 while (tc
> tc_high
) {
561 ale_real sf
= pow(2, -tc_high
);
563 weights
.push_back(make_image(sf
, -1));
566 add_weight((int) tc
, p
, weight
, st
);
572 ale_real
get_weight(int tc
, unsigned int i
, unsigned int j
) {
573 d2::image
*im
= weights
[tc
- tc_low
];
575 assert(i
< im
->height());
576 assert(j
< im
->width());
578 if (im
->pix(i
, j
)[0] == -1) {
579 return (get_weight(tc
- 1, i
* 2 + 0, j
* 2 + 0)
580 + get_weight(tc
- 1, i
* 2 + 1, j
* 2 + 0)
581 + get_weight(tc
- 1, i
* 2 + 1, j
* 2 + 1)
582 + get_weight(tc
- 1, i
* 2 + 0, j
* 2 + 1)) / 4;
585 if (im
->pix(i
, j
)[0] == 0) {
586 return get_weight(tc
+ 1, i
/ 2, j
/ 2);
589 return im
->pix(i
, j
)[0];
592 ale_real
get_weight(int tc
, d2::point p
) {
596 unsigned int i
= (unsigned int) floor(p
[0]);
597 unsigned int j
= (unsigned int) floor(p
[1]);
599 return get_weight(tc
, i
, j
);
602 ale_real
get_weight(const space::traverse
&t
) {
603 ale_pos tc
= transformation
.trilinear_coordinate(t
);
604 d2::point p
= transformation
.wp_unscaled(t
.get_centroid()).xy();
605 assert(in_spatial_bounds(p
));
617 return get_weight((int) tc
, p
);
620 assert(tc
> tc_high
);
622 int multiplier
= (int) pow(2, (tc
- tc_high
));
625 for (int i
= 0; i
< multiplier
; i
++)
626 for (int j
= 0; j
< multiplier
; j
++) {
627 result
+= get_weight(tc_high
,
628 (unsigned int) floor(p
[0]) * multiplier
+ i
,
629 (unsigned int) floor(p
[1]) * multiplier
+ j
);
636 * Check whether a subtree is simple.
638 int is_simple(subtree
*s
) {
641 if (s
->node_value
== -1
642 && s
->children
[0][0] == NULL
643 && s
->children
[0][1] == NULL
644 && s
->children
[1][0] == NULL
645 && s
->children
[1][1] == NULL
)
652 * Get a weight subtree.
654 subtree
*get_subtree(int tc
, unsigned int i
, unsigned int j
) {
657 * tc > tc_high is handled recursively.
661 subtree
*result
= new subtree(-1,
662 get_subtree(tc
- 1, i
* 2 + 0, j
* 2 + 0),
663 get_subtree(tc
- 1, i
* 2 + 0, j
* 2 + 1),
664 get_subtree(tc
- 1, i
* 2 + 1, j
* 2 + 0),
665 get_subtree(tc
- 1, i
* 2 + 1, j
* 2 + 1));
667 if (is_simple(result
)) {
675 assert(tc
>= tc_low
);
676 assert(weights
[tc
- tc_low
]);
678 d2::image
*im
= weights
[tc
- tc_low
];
681 * Rectangular images will, in general, have
682 * out-of-bounds tree sections. Handle this case.
685 if (i
>= im
->height())
687 if (j
>= im
->width())
691 * -1 weights are handled recursively
694 if (im
->pix(i
, j
)[0] == -1) {
695 subtree
*result
= new subtree(-1,
696 get_subtree(tc
- 1, i
* 2 + 0, j
* 2 + 0),
697 get_subtree(tc
- 1, i
* 2 + 0, j
* 2 + 1),
698 get_subtree(tc
- 1, i
* 2 + 1, j
* 2 + 0),
699 get_subtree(tc
- 1, i
* 2 + 1, j
* 2 + 1));
701 if (is_simple(result
)) {
702 im
->pix(i
, j
)[0] = 0;
711 * Zero weights have NULL subtrees.
714 if (im
->pix(i
, j
)[0] == 0)
718 * Handle the remaining case.
721 return new subtree(im
->pix(i
, j
)[0], NULL
, NULL
, NULL
, NULL
);
724 subtree
*get_subtree(int tc
, d2::point p
) {
727 unsigned int i
= (unsigned int) floor(p
[0]);
728 unsigned int j
= (unsigned int) floor(p
[1]);
730 return get_subtree(tc
, i
, j
);
733 subtree
*get_subtree(const space::traverse
&t
) {
734 ale_pos tc
= transformation
.trilinear_coordinate(t
);
735 d2::point p
= transformation
.wp_unscaled(t
.get_centroid()).xy();
736 assert(in_spatial_bounds(p
));
741 if (tc
< input_decimation_lower
)
742 tc
= input_decimation_lower
;
749 return get_subtree((int) tc
, p
);
756 for (unsigned int i
= 0; i
< weights
.size(); i
++) {
765 static int resolution_ok(pt transformation
, ale_pos tc
) {
767 if (pow(2, tc
) > transformation
.unscaled_height()
768 || pow(2, tc
) > transformation
.unscaled_width())
771 if (tc
< input_decimation_lower
)
778 * Structure to hold input frame information at all levels of detail.
786 std::vector
<lod_image
*> images
;
791 images
.resize(d2::image_rw::count(), NULL
);
794 void open(unsigned int f
) {
795 assert (images
[f
] == NULL
);
797 if (images
[f
] == NULL
)
798 images
[f
] = new lod_image(f
);
802 for (unsigned int f
= 0; f
< d2::image_rw::count(); f
++)
806 lod_image
*get(unsigned int f
) {
807 assert (images
[f
] != NULL
);
811 void close(unsigned int f
) {
812 assert (images
[f
] != NULL
);
818 for (unsigned int f
= 0; f
< d2::image_rw::count(); f
++)
828 * All levels-of-detail
831 static struct lod_images
*al
;
834 * Data structure for storing best encountered subspace candidates.
837 std::vector
<std::vector
<std::pair
<ale_pos
, ale_real
> > > levels
;
843 * Point p is in world coordinates.
845 void generate_subspace(point iw
, ale_pos diagonal
) {
847 space::traverse st
= space::traverse::root();
849 if (!st
.includes(iw
)) {
858 * Loop until resolutions of interest have been generated.
863 ale_pos current_diagonal
= (st
.get_max() - st
.get_min()).norm();
865 assert(!isnan(current_diagonal
));
868 * Generate any new desired spatial registers.
875 for (int f
= 0; f
< 2; f
++) {
881 if (current_diagonal
< 2 * diagonal
883 spatial_info_map
[st
.get_node()];
891 if (current_diagonal
< diagonal
893 spatial_info_map
[st
.get_node()];
899 * Check for completion
902 if (highres
&& lowres
)
906 * Check precision before analyzing space further.
909 if (st
.precision_wall()) {
910 fprintf(stderr
, "\n\n*** Error: reached subspace precision wall ***\n\n");
915 if (st
.positive().includes(iw
)) {
918 } else if (st
.negative().includes(iw
)) {
922 fprintf(stderr
, "failed iw = (%f, %f, %f)\n",
923 iw
[0], iw
[1], iw
[2]);
933 height
= (unsigned int) al
->get(f
)->get_t(0).unscaled_height();
934 width
= (unsigned int) al
->get(f
)->get_t(0).unscaled_width();
940 levels
.resize(primary_decimation_upper
- input_decimation_lower
+ 1);
941 for (int l
= input_decimation_lower
; l
<= primary_decimation_upper
; l
++) {
942 for (unsigned int i
= 0; i
< (unsigned int) floor(height
/ pow(2, l
)); i
++)
943 for (unsigned int j
= 0; j
< (unsigned int) floor(width
/ pow(2, l
)); j
++)
944 for (unsigned int k
= 0; k
< pairwise_ambiguity
; k
++) {
945 levels
[l
][i
* width
* pairwise_ambiguity
+ j
* pairwise_ambiguity
+ k
] =
946 std::pair
<ale_pos
, ale_real
>(0, 0);
952 * Point p is expected to be in local projective coordinates.
955 void add_candidate(point p
, int tc
, ale_real score
) {
956 assert(tc
<= primary_decimation_upper
);
957 assert(tc
>= input_decimation_lower
);
961 int i
= (unsigned int) floor(p
[0] / pow(2, tc
));
962 int j
= (unsigned int) floor(p
[1] / pow(2, tc
));
964 assert(i
< floor(height
/ pow(2, tc
)));
965 assert(j
< floor(width
/ pow(2, tc
)));
967 for (unsigned int k
= 0; k
< pairwise_ambiguity
; k
++) {
968 std::pair
<ale_pos
, ale_real
> *pk
=
969 &(levels
[tc
][i
* width
* pairwise_ambiguity
+ j
* pairwise_ambiguity
]);
971 if (pk
->first
!= 0 && score
>= pk
->second
)
974 ale_pos tp
= pk
->first
;
975 ale_real tr
= pk
->second
;
986 * Generate subspaces for candidates.
989 void generate_subspaces() {
990 for (int l
= input_decimation_lower
; l
<= primary_decimation_upper
; l
++) {
991 for (unsigned int i
= 0; i
< (unsigned int) floor(height
/ pow(2, l
)); i
++)
992 for (unsigned int j
= 0; j
< (unsigned int) floor(width
/ pow(2, l
)); j
++)
993 for (unsigned int k
= 0; k
< pairwise_ambiguity
; k
++) {
994 std::pair
<ale_pos
, ale_real
> *pk
=
995 &(levels
[l
- input_decimation_lower
]
996 [i
* width
* pairwise_ambiguity
+ j
* pairwise_ambiguity
]);
1001 ale_pos si
= i
* pow(2, l
) + ((l
> 0) ? pow(2, l
- 1) : 0);
1002 ale_pos sj
= j
* pow(2, l
) + ((l
> 0) ? pow(2, l
- 1) : 0);
1004 point p
= al
->get(image_index
)->get_t(0).pw_unscaled(point(si
, sj
, pk
->first
));
1006 generate_subspace(p
,
1007 al
->get(image_index
)->get_t(0).diagonal_distance_3d(pk
->first
, l
));
1014 * List for calculating weighted median.
1021 ale_real
&_w(unsigned int i
) {
1026 ale_real
&_d(unsigned int i
) {
1028 return data
[i
* 2 + 1];
1031 void increase_capacity() {
1038 data
= (ale_real
*) realloc(data
, sizeof(ale_real
) * 2 * (size
* 1));
1041 assert (size
> used
);
1044 fprintf(stderr
, "Unable to allocate %d bytes of memory\n",
1045 sizeof(ale_real
) * 2 * (size
* 1));
1050 void insert_weight(unsigned int i
, ale_real v
, ale_real w
) {
1051 assert(used
< size
);
1053 for (unsigned int j
= used
; j
> i
; j
--) {
1066 unsigned int get_size() {
1070 unsigned int get_used() {
1075 fprintf(stderr
, "[st %p sz %u el", this, size
);
1076 for (unsigned int i
= 0; i
< used
; i
++)
1077 fprintf(stderr
, " (%f, %f)", _d(i
), _w(i
));
1078 fprintf(stderr
, "]\n");
1085 void insert_weight(ale_real v
, ale_real w
) {
1086 for (unsigned int i
= 0; i
< used
; i
++) {
1093 increase_capacity();
1094 insert_weight(i
, v
, w
);
1099 increase_capacity();
1100 insert_weight(used
, v
, w
);
1104 * Finds the median at half-weight, or between half-weight
1105 * and zero-weight, depending on the attenuation value.
1108 ale_real
find_median(double attenuation
= 0) {
1110 assert(attenuation
>= 0);
1111 assert(attenuation
<= 1);
1115 ale_real undefined
= zero1
/ zero2
;
1117 ale_accum weight_sum
= 0;
1119 for (unsigned int i
= 0; i
< used
; i
++)
1120 weight_sum
+= _w(i
);
1122 // if (weight_sum == 0)
1123 // return undefined;
1125 if (used
== 0 || used
== 1)
1128 if (weight_sum
== 0) {
1129 ale_accum data_sum
= 0;
1130 for (unsigned int i
= 0; i
< used
; i
++)
1132 return data_sum
/ used
;
1136 ale_accum midpoint
= weight_sum
* (0.5 - 0.5 * attenuation
);
1138 ale_accum weight_sum_2
= 0;
1140 for (unsigned int i
= 0; i
< used
&& weight_sum_2
< midpoint
; i
++) {
1141 weight_sum_2
+= _w(i
);
1143 if (weight_sum_2
> midpoint
) {
1145 } else if (weight_sum_2
== midpoint
) {
1146 assert (i
+ 1 < used
);
1147 return (_d(i
) + _d(i
+ 1)) / 2;
1154 wml(int initial_size
= 0) {
1156 // if (initial_size == 0) {
1157 // initial_size = (int) (d2::image_rw::count() * 1.5);
1160 size
= initial_size
;
1164 data
= (ale_real
*) malloc(size
* sizeof(ale_real
) * 2);
1172 * copy constructor. This is required to avoid undesired frees.
1178 data
= (ale_real
*) malloc(size
* sizeof(ale_real
) * 2);
1181 memcpy(data
, w
.data
, size
* sizeof(ale_real
) * 2);
1190 * Class for information regarding spatial regions of interest.
1192 * This class is configured for convenience in cases where sampling is
1193 * performed using an approximation of the fine:box:1,triangle:2 chain.
1194 * In this case, the *_1 variables would store the fine data and the
1195 * *_2 variables would store the coarse data. Other uses are also
1199 class spatial_info
{
1201 * Map channel value --> weight.
1203 wml color_weights_1
[3];
1204 wml color_weights_2
[3];
1212 * Map occupancy value --> weight.
1214 wml occupancy_weights_1
;
1215 wml occupancy_weights_2
;
1218 * Current occupancy value.
1226 unsigned int pocc_density
;
1227 unsigned int socc_density
;
1230 * Insert a weight into a list.
1232 void insert_weight(wml
*m
, ale_real v
, ale_real w
) {
1233 m
->insert_weight(v
, w
);
1237 * Find the median of a weighted list. Uses NaN for undefined.
1239 ale_real
find_median(wml
*m
, double attenuation
= 0) {
1240 return m
->find_median(attenuation
);
1248 color
= d2::pixel::zero();
1255 * Accumulate color; primary data set.
1257 void accumulate_color_1(int f
, d2::pixel color
, d2::pixel weight
) {
1258 for (int k
= 0; k
< 3; k
++)
1259 insert_weight(&color_weights_1
[k
], color
[k
], weight
[k
]);
1263 * Accumulate color; secondary data set.
1265 void accumulate_color_2(d2::pixel color
, d2::pixel weight
) {
1266 for (int k
= 0; k
< 3; k
++)
1267 insert_weight(&color_weights_2
[k
], color
[k
], weight
[k
]);
1271 * Accumulate occupancy; primary data set.
1273 void accumulate_occupancy_1(int f
, ale_real occupancy
, ale_real weight
) {
1274 insert_weight(&occupancy_weights_1
, occupancy
, weight
);
1278 * Accumulate occupancy; secondary data set.
1280 void accumulate_occupancy_2(ale_real occupancy
, ale_real weight
) {
1281 insert_weight(&occupancy_weights_2
, occupancy
, weight
);
1283 if (occupancy
== 0 || occupancy_weights_2
.get_size() < 96)
1286 // fprintf(stderr, "%p updated socc with: %f %f\n", this, occupancy, weight);
1287 // occupancy_weights_2.print_info();
1291 * Update color (and clear accumulation structures).
1293 void update_color() {
1294 for (int d
= 0; d
< 3; d
++) {
1295 ale_real c
= find_median(&color_weights_1
[d
]);
1297 c
= find_median(&color_weights_2
[d
]);
1303 color_weights_1
[d
].clear();
1304 color_weights_2
[d
].clear();
1309 * Update occupancy (and clear accumulation structures).
1311 void update_occupancy() {
1312 ale_real o
= find_median(&occupancy_weights_1
, occ_att
);
1314 o
= find_median(&occupancy_weights_2
, occ_att
);
1320 pocc_density
= occupancy_weights_1
.get_used();
1321 socc_density
= occupancy_weights_2
.get_used();
1323 occupancy_weights_1
.clear();
1324 occupancy_weights_2
.clear();
1329 * Get current color.
1331 d2::pixel
get_color() {
1336 * Get current occupancy.
1338 ale_real
get_occupancy() {
1343 * Get primary color density.
1346 unsigned int get_pocc_density() {
1347 return pocc_density
;
1350 unsigned int get_socc_density() {
1351 return socc_density
;
1356 * Map spatial regions of interest to spatial info structures. XXX:
1357 * This may get very poor cache behavior in comparison with, say, an
1358 * array. Unfortunately, there is no immediately obvious array
1359 * representation. If some kind of array representation were adopted,
1360 * it would probably cluster regions of similar depth from the
1361 * perspective of the typical camera. In particular, for a
1362 * stereoscopic view, depth ordering for two random points tends to be
1363 * similar between cameras, I think. Unfortunately, it is never
1364 * identical for all points (unless cameras are co-located). One
1365 * possible approach would be to order based on, say, camera 0's idea
1369 typedef std::map
<struct space::node
*, spatial_info
> spatial_info_map_t
;
1371 static spatial_info_map_t spatial_info_map
;
1376 * Debugging variables.
1379 static unsigned long total_ambiguity
;
1380 static unsigned long total_pixels
;
1381 static unsigned long total_divisions
;
1382 static unsigned long total_tsteps
;
1388 static void et(double et_parameter
) {
1389 encounter_threshold
= et_parameter
;
1392 static void load_model(const char *name
) {
1393 load_model_name
= name
;
1396 static void save_model(const char *name
) {
1397 save_model_name
= name
;
1400 static void fc(ale_pos fc
) {
1404 static void di_upper(ale_pos _dgi
) {
1405 primary_decimation_upper
= (int) round(_dgi
);
1408 static void do_try(ale_pos _dgo
) {
1409 output_decimation_preferred
= (int) round(_dgo
);
1412 static void di_lower(ale_pos _idiv
) {
1413 input_decimation_lower
= (int) round(_idiv
);
1420 static void no_oc() {
1424 static void rc(ale_pos rc
) {
1429 * Initialize 3D scene from 2D scene, using 2D and 3D alignment
1432 static void init_from_d2() {
1435 * Rear clip value of 0 is converted to infinity.
1438 if (rear_clip
== 0) {
1442 rear_clip
= one
/ zero
;
1443 assert(isinf(rear_clip
) == +1);
1447 * Scale and translate clipping plane depths.
1450 ale_pos cp_scalar
= d3::align::projective(0).wc(point(0, 0, 0))[2];
1452 front_clip
= front_clip
* cp_scalar
- cp_scalar
;
1453 rear_clip
= rear_clip
* cp_scalar
- cp_scalar
;
1457 * Allocate image structures.
1460 al
= new lod_images
;
1462 if (tc_multiplier
!= 0) {
1468 * Perform spatial_info updating on a given subspace, for given
1471 static void subspace_info_update(space::iterate si
, int f
, ref_weights
*weights
) {
1474 space::traverse st
= si
.get();
1477 * Skip spaces with no color information.
1479 * XXX: This could be more efficient, perhaps.
1482 if (spatial_info_map
.count(st
.get_node()) == 0) {
1488 * Get in-bounds centroid, if one exists.
1491 point p
= al
->get(f
)->get_t(0).centroid(st
);
1499 * Get information on the subspace.
1502 spatial_info
*sn
= &spatial_info_map
[st
.get_node()];
1503 d2::pixel color
= sn
->get_color();
1504 ale_real occupancy
= sn
->get_occupancy();
1507 * Store current weight so we can later check for
1508 * modification by higher-resolution subspaces.
1511 ref_weights::subtree
*tree
= weights
->get_subtree(st
);
1514 * Check for higher resolution subspaces, and
1515 * update the space iterator.
1518 if (st
.get_node()->positive
1519 || st
.get_node()->negative
) {
1522 * Cleave space for the higher-resolution pass,
1523 * skipping the current space, since we will
1524 * process that later.
1527 space::iterate cleaved_space
= si
.cleave();
1529 cleaved_space
.next();
1531 subspace_info_update(cleaved_space
, f
, weights
);
1538 * Add new data on the subspace and update weights.
1541 ale_pos tc
= al
->get(f
)->get_t(0).trilinear_coordinate(st
);
1542 d2::pixel pcolor
= al
->get(f
)->get_tl(p
.xy(), tc
);
1543 d2::pixel colordiff
= (color
- pcolor
) * (ale_real
) 256;
1545 if (falloff_exponent
!= 0) {
1546 d2::pixel max_diff
= al
->get(f
)->get_max_diff(p
.xy(), tc
) * (ale_real
) 256;
1548 for (int k
= 0; k
< 3; k
++)
1549 if (max_diff
[k
] > 1)
1550 colordiff
[k
] /= pow(max_diff
[k
], falloff_exponent
);
1554 * Determine the probability of
1555 * encounter, divided by the occupancy.
1558 d2::pixel encounter
= d2::pixel(1, 1, 1) * (1 - weights
->get_weight(st
));
1564 weights
->add_weight(st
, (encounter
* occupancy
)[0], tree
);
1567 * Delete the subtree, if necessary.
1573 * Check for cases in which the subspace should not be
1577 if (!resolution_ok(al
->get(f
)->get_t(0), tc
))
1584 sn
->accumulate_color_1(f
, pcolor
, encounter
);
1585 d2::pixel channel_occ
= pexp(-colordiff
* colordiff
);
1587 ale_accum occ
= channel_occ
[0];
1589 for (int k
= 1; k
< 3; k
++)
1590 if (channel_occ
[k
] < occ
)
1591 occ
= channel_occ
[k
];
1593 sn
->accumulate_occupancy_1(f
, occ
, encounter
[0]);
1599 * Run a single iteration of the spatial_info update cycle.
1601 static void spatial_info_update() {
1603 * Iterate through each frame.
1605 for (unsigned int f
= 0; f
< d2::image_rw::count(); f
++) {
1608 * Open the frame and transformation.
1611 if (tc_multiplier
== 0)
1615 * Allocate weights data structure for storing encounter
1619 ref_weights
*weights
= new ref_weights(f
);
1622 * Call subspace_info_update for the root space.
1625 subspace_info_update(space::iterate(al
->get(f
)->origin()), f
, weights
);
1634 * Close the frame and transformation.
1637 if (tc_multiplier
== 0)
1642 * Update all spatial_info structures.
1644 for (spatial_info_map_t::iterator i
= spatial_info_map
.begin(); i
!= spatial_info_map
.end(); i
++) {
1645 i
->second
.update_color();
1646 i
->second
.update_occupancy();
1648 // d2::pixel color = i->second.get_color();
1650 // fprintf(stderr, "space p=%p updated to c=[%f %f %f] o=%f\n",
1651 // i->first, color[0], color[1], color[2],
1652 // i->second.get_occupancy());
1657 * Support function for view() and depth().
1660 static const void view_recurse(int type
, d2::image
*im
, d2::image
*weights
, space::iterate si
, pt _pt
) {
1661 while (!si
.done()) {
1662 space::traverse st
= si
.get();
1665 * XXX: This could be more efficient, perhaps.
1668 if (spatial_info_map
.count(st
.get_node()) == 0) {
1673 spatial_info sn
= spatial_info_map
[st
.get_node()];
1676 * Get information on the subspace.
1679 d2::pixel color
= sn
.get_color();
1680 // d2::pixel color = d2::pixel(1, 1, 1) * (double) (((unsigned int) (st.get_node()) / sizeof(space)) % 65535);
1681 ale_real occupancy
= sn
.get_occupancy();
1684 * Determine the view-local bounding box for the
1690 _pt
.get_view_local_bb(st
, bb
);
1696 * Data structure to check modification of weights by
1697 * higher-resolution subspaces.
1700 std::queue
<d2::pixel
> weight_queue
;
1703 * Check for higher resolution subspaces, and
1704 * update the space iterator.
1707 if (st
.get_node()->positive
1708 || st
.get_node()->negative
) {
1711 * Store information about current weights,
1712 * so we will know which areas have been
1713 * covered by higher-resolution subspaces.
1716 for (int i
= (int) ceil(min
[0]); i
<= (int) floor(max
[0]); i
++)
1717 for (int j
= (int) ceil(min
[1]); j
<= (int) floor(max
[1]); j
++)
1718 weight_queue
.push(weights
->get_pixel(i
, j
));
1721 * Cleave space for the higher-resolution pass,
1722 * skipping the current space, since we will
1723 * process that afterward.
1726 space::iterate cleaved_space
= si
.cleave();
1728 cleaved_space
.next();
1730 view_recurse(type
, im
, weights
, cleaved_space
, _pt
);
1738 * Iterate over pixels in the bounding box, finding
1739 * pixels that intersect the subspace. XXX: assume
1740 * for now that all pixels in the bounding box
1741 * intersect the subspace.
1744 for (int i
= (int) ceil(min
[0]); i
<= (int) floor(max
[0]); i
++)
1745 for (int j
= (int) ceil(min
[1]); j
<= (int) floor(max
[1]); j
++) {
1748 * Check for higher-resolution updates.
1751 if (weight_queue
.size()) {
1752 if (weight_queue
.front() != weights
->get_pixel(i
, j
)) {
1760 * Determine the probability of encounter.
1763 d2::pixel encounter
= (d2::pixel(1, 1, 1) - weights
->get_pixel(i
, j
)) * occupancy
;
1775 weights
->pix(i
, j
) += encounter
;
1776 im
->pix(i
, j
) += encounter
* color
;
1778 } else if (type
== 1) {
1781 * Weighted (transparent) depth display
1784 ale_pos depth_value
= _pt
.wp_scaled(st
.get_min())[2];
1785 weights
->pix(i
, j
) += encounter
;
1786 im
->pix(i
, j
) += encounter
* depth_value
;
1788 } else if (type
== 2) {
1791 * Ambiguity (ambivalence) measure.
1794 weights
->pix(i
, j
) = d2::pixel(1, 1, 1);
1795 im
->pix(i
, j
) += 0.1 * d2::pixel(1, 1, 1);
1797 } else if (type
== 3) {
1800 * Closeness measure.
1803 ale_pos depth_value
= _pt
.wp_scaled(st
.get_min())[2];
1804 if (weights
->pix(i
, j
)[0] == 0) {
1805 weights
->pix(i
, j
) = d2::pixel(1, 1, 1);
1806 im
->pix(i
, j
) = d2::pixel(1, 1, 1) * depth_value
;
1807 } else if (im
->pix(i
, j
)[2] < depth_value
) {
1808 im
->pix(i
, j
) = d2::pixel(1, 1, 1) * depth_value
;
1813 } else if (type
== 4) {
1816 * Weighted (transparent) contribution display
1819 ale_pos contribution_value
= sn
.get_pocc_density() + sn
.get_socc_density();
1820 weights
->pix(i
, j
) += encounter
;
1821 im
->pix(i
, j
) += encounter
* contribution_value
;
1823 } else if (type
== 5) {
1826 * Weighted (transparent) occupancy display
1829 ale_pos contribution_value
= occupancy
;
1830 weights
->pix(i
, j
) += encounter
;
1831 im
->pix(i
, j
) += encounter
* contribution_value
;
1833 } else if (type
== 6) {
1836 * (Depth, xres, yres) triple
1839 ale_pos depth_value
= _pt
.wp_scaled(st
.get_min())[2];
1840 weights
->pix(i
, j
)[0] += encounter
[0];
1841 if (weights
->pix(i
, j
)[1] < encounter
[0]) {
1842 weights
->pix(i
, j
)[1] = encounter
[0];
1843 im
->pix(i
, j
)[0] = weights
->pix(i
, j
)[1] * depth_value
;
1844 im
->pix(i
, j
)[1] = max
[0] - min
[0];
1845 im
->pix(i
, j
)[2] = max
[1] - min
[1];
1848 } else if (type
== 7) {
1851 * (xoff, yoff, 0) triple
1854 weights
->pix(i
, j
)[0] += encounter
[0];
1855 if (weights
->pix(i
, j
)[1] < encounter
[0]) {
1856 weights
->pix(i
, j
)[1] = encounter
[0];
1857 im
->pix(i
, j
)[0] = i
- min
[0];
1858 im
->pix(i
, j
)[1] = j
- min
[1];
1859 im
->pix(i
, j
)[2] = 0;
1869 * Generate an depth image from a specified view.
1871 static const d2::image
*depth(pt _pt
, int n
= -1) {
1872 assert ((unsigned int) n
< d2::image_rw::count() || n
< 0);
1875 assert((int) floor(d2::align::of(n
).scaled_height())
1876 == (int) floor(_pt
.scaled_height()));
1877 assert((int) floor(d2::align::of(n
).scaled_width())
1878 == (int) floor(_pt
.scaled_width()));
1881 d2::image
*im1
= new d2::image_ale_real((int) floor(_pt
.scaled_height()),
1882 (int) floor(_pt
.scaled_width()), 3);
1884 d2::image
*im2
= new d2::image_ale_real((int) floor(_pt
.scaled_height()),
1885 (int) floor(_pt
.scaled_width()), 3);
1887 d2::image
*im3
= new d2::image_ale_real((int) floor(_pt
.scaled_height()),
1888 (int) floor(_pt
.scaled_width()), 3);
1890 _pt
.view_angle(_pt
.view_angle() * VIEW_ANGLE_MULTIPLIER
);
1893 * Use adaptive subspace data.
1896 d2::image
*weights
= new d2::image_ale_real((int) floor(_pt
.scaled_height()),
1897 (int) floor(_pt
.scaled_width()), 3);
1900 * Iterate through subspaces.
1903 space::iterate
si(_pt
.origin());
1905 view_recurse(6, im1
, weights
, si
, _pt
);
1908 weights
= new d2::image_ale_real((int) floor(_pt
.scaled_height()),
1909 (int) floor(_pt
.scaled_width()), 3);
1911 view_recurse(7, im2
, weights
, si
, _pt
);
1914 * Normalize depths by weights
1917 if (normalize_weights
)
1918 for (unsigned int i
= 0; i
< im1
->height(); i
++)
1919 for (unsigned int j
= 0; j
< im1
->width(); j
++)
1920 im1
->pix(i
, j
)[0] /= weights
->pix(i
, j
)[1];
1923 for (unsigned int i
= 0; i
< im1
->height(); i
++)
1924 for (unsigned int j
= 0; j
< im1
->width(); j
++) {
1927 * Handle interpolation.
1932 d2::point
res(im1
->pix(i
, j
)[1], im1
->pix(i
, j
)[2]);
1934 for (int d
= 0; d
< 2; d
++) {
1936 if (im2
->pix(i
, j
)[d
] < res
[d
] / 2)
1937 x
[d
] = (ale_pos
) (d
?j
:i
) - res
[d
] / 2 - im2
->pix(i
, j
)[d
];
1939 x
[d
] = (ale_pos
) (d
?j
:i
) + res
[d
] / 2 - im2
->pix(i
, j
)[d
];
1941 blx
[d
] = 1 - ((d
?j
:i
) - x
[d
]) / res
[d
];
1944 ale_real depth_val
= 0;
1945 ale_real depth_weight
= 0;
1947 for (int ii
= 0; ii
< 2; ii
++)
1948 for (int jj
= 0; jj
< 2; jj
++) {
1949 d2::point p
= x
+ d2::point(ii
, jj
) * res
;
1950 if (im1
->in_bounds(p
)) {
1952 ale_real d
= im1
->get_bl(p
)[0];
1957 ale_real w
= ((ii
? (1 - blx
[0]) : blx
[0]) * (jj
? (1 - blx
[1]) : blx
[1]));
1963 ale_real depth
= depth_val
/ depth_weight
;
1966 * Handle exclusions and encounter thresholds
1969 point w
= _pt
.pw_scaled(point(i
, j
, depth
));
1971 if (weights
->pix(i
, j
)[0] < encounter_threshold
|| excluded(w
)) {
1972 im3
->pix(i
, j
) = d2::pixel::zero() / d2::pixel::zero();
1974 im3
->pix(i
, j
) = d2::pixel(1, 1, 1) * depth
;
1985 static const d2::image
*depth(unsigned int n
) {
1987 assert (n
< d2::image_rw::count());
1989 pt _pt
= align::projective(n
);
1991 return depth(_pt
, n
);
1995 * Generate an image from a specified view.
1997 static const d2::image
*view(pt _pt
, int n
= -1) {
1998 assert ((unsigned int) n
< d2::image_rw::count() || n
< 0);
2001 assert((int) floor(d2::align::of(n
).scaled_height())
2002 == (int) floor(_pt
.scaled_height()));
2003 assert((int) floor(d2::align::of(n
).scaled_width())
2004 == (int) floor(_pt
.scaled_width()));
2007 const d2::image
*depths
= NULL
;
2010 depths
= depth(_pt
, n
);
2012 d2::image
*im
= new d2::image_ale_real((int) floor(_pt
.scaled_height()),
2013 (int) floor(_pt
.scaled_width()), 3);
2015 _pt
.view_angle(_pt
.view_angle() * VIEW_ANGLE_MULTIPLIER
);
2018 * Use adaptive subspace data.
2021 d2::image
*weights
= new d2::image_ale_real((int) floor(_pt
.scaled_height()),
2022 (int) floor(_pt
.scaled_width()), 3);
2025 * Iterate through subspaces.
2028 space::iterate
si(_pt
.origin());
2030 view_recurse(0, im
, weights
, si
, _pt
);
2032 for (unsigned int i
= 0; i
< im
->height(); i
++)
2033 for (unsigned int j
= 0; j
< im
->width(); j
++) {
2034 if (weights
->pix(i
, j
).min_norm() < encounter_threshold
2035 || (d3px_count
> 0 && isnan(depths
->pix(i
, j
)[0]))) {
2036 im
->pix(i
, j
) = d2::pixel::zero() / d2::pixel::zero();
2037 weights
->pix(i
, j
) = d2::pixel::zero();
2038 } else if (normalize_weights
)
2039 im
->pix(i
, j
) /= weights
->pix(i
, j
);
2050 static void tcem(double _tcem
) {
2051 tc_multiplier
= _tcem
;
2054 static void oui(unsigned int _oui
) {
2055 ou_iterations
= _oui
;
2058 static void pa(unsigned int _pa
) {
2059 pairwise_ambiguity
= _pa
;
2062 static void pc(const char *_pc
) {
2063 pairwise_comparisons
= _pc
;
2066 static void d3px(int _d3px_count
, double *_d3px_parameters
) {
2067 d3px_count
= _d3px_count
;
2068 d3px_parameters
= _d3px_parameters
;
2071 static void fx(double _fx
) {
2072 falloff_exponent
= _fx
;
2076 normalize_weights
= 1;
2079 static void no_nw() {
2080 normalize_weights
= 0;
2083 static int excluded(point p
) {
2084 for (int n
= 0; n
< d3px_count
; n
++) {
2085 double *region
= d3px_parameters
+ (6 * n
);
2086 if (p
[0] >= region
[0]
2087 && p
[0] <= region
[1]
2088 && p
[1] >= region
[2]
2089 && p
[1] <= region
[3]
2090 && p
[2] >= region
[4]
2091 && p
[2] <= region
[5])
2098 static const d2::image
*view(unsigned int n
) {
2100 assert (n
< d2::image_rw::count());
2102 pt _pt
= align::projective(n
);
2104 return view(_pt
, n
);
2108 * Add specified control points to the model
2110 static void add_control_points() {
2113 typedef struct {point iw
; point ip
, is
;} analytic
;
2114 typedef std::multimap
<ale_real
,analytic
> score_map
;
2115 typedef std::pair
<ale_real
,analytic
> score_map_element
;
2117 static void solve_point_pair(unsigned int f1
, unsigned int f2
, int i
, int j
, ale_pos ii
, ale_pos jj
,
2118 ale_real
*normalized_score
, analytic
*_a
,
2119 const pt
&_pt1
, const pt
&_pt2
, const d2::image
*if1
, const d2::image
*if2
) {
2121 *normalized_score
= 0;
2122 *normalized_score
/= *normalized_score
;
2123 assert(isnan(*normalized_score
));
2126 * Create an orthonormal basis to
2127 * determine line intersection.
2130 point bp0
= _pt1
.pw_scaled(point(i
, j
, 0));
2131 point bp1
= _pt1
.pw_scaled(point(i
, j
, 10));
2132 point bp2
= _pt2
.pw_scaled(point(ii
, jj
, 0));
2134 point b0
= (bp1
- bp0
).normalize();
2135 point b1n
= bp2
- bp0
;
2136 point b1
= (b1n
- b1n
.dproduct(b0
) * b0
).normalize();
2137 point b2
= point(0, 0, 0).xproduct(b0
, b1
).normalize(); // Should already have norm=1
2140 * Select a fourth point to define a second line.
2143 point p3
= _pt2
.pw_scaled(point(ii
, jj
, 10));
2146 * Representation in the new basis.
2149 d2::point nbp0
= d2::point(bp0
.dproduct(b0
), bp0
.dproduct(b1
));
2150 // d2::point nbp1 = d2::point(bp1.dproduct(b0), bp1.dproduct(b1));
2151 d2::point nbp2
= d2::point(bp2
.dproduct(b0
), bp2
.dproduct(b1
));
2152 d2::point np3
= d2::point( p3
.dproduct(b0
), p3
.dproduct(b1
));
2155 * Determine intersection of line
2156 * (nbp0, nbp1), which is parallel to
2157 * b0, with line (nbp2, np3).
2161 * XXX: a stronger check would be
2162 * better here, e.g., involving the
2163 * ratio (np3[0] - nbp2[0]) / (np3[1] -
2164 * nbp2[1]). Also, acceptance of these
2165 * cases is probably better than
2169 if (np3
[1] - nbp2
[1] == 0)
2172 d2::point intersection
= d2::point(nbp2
[0]
2173 + (nbp0
[1] - nbp2
[1]) * (np3
[0] - nbp2
[0]) / (np3
[1] - nbp2
[1]),
2176 ale_pos b2_offset
= b2
.dproduct(bp0
);
2179 * Map the intersection back to the world
2183 point iw
= intersection
[0] * b0
+ intersection
[1] * b1
+ b2_offset
* b2
;
2186 * Reject intersection points behind a
2190 point icp
= _pt1
.wc(iw
);
2191 point ics
= _pt2
.wc(iw
);
2194 if (icp
[2] >= 0 || ics
[2] >= 0)
2198 * Reject clipping plane violations.
2201 if (iw
[2] > front_clip
2202 || iw
[2] < rear_clip
)
2209 point ip
= _pt1
.wp_scaled(iw
);
2211 point is
= _pt2
.wp_scaled(iw
);
2218 * Calculate score from color match. Assume for now
2219 * that the transformation can be approximated locally
2220 * with a translation.
2224 ale_pos divisor
= 0;
2225 ale_pos l1_multiplier
= 0.125;
2227 if (if1
->in_bounds(ip
.xy())
2228 && if2
->in_bounds(is
.xy())) {
2229 divisor
+= 1 - l1_multiplier
;
2230 score
+= (1 - l1_multiplier
)
2231 * (if1
->get_bl(ip
.xy()) - if2
->get_bl(is
.xy())).normsq();
2234 for (int iii
= -1; iii
<= 1; iii
++)
2235 for (int jjj
= -1; jjj
<= 1; jjj
++) {
2236 d2::point
t(iii
, jjj
);
2238 if (!if1
->in_bounds(ip
.xy() + t
)
2239 || !if2
->in_bounds(is
.xy() + t
))
2242 divisor
+= l1_multiplier
;
2243 score
+= l1_multiplier
2244 * (if1
->get_bl(ip
.xy() + t
) - if2
->get_bl(is
.xy() + t
)).normsq();
2249 * Include third-camera contributions in the score.
2252 if (tc_multiplier
!= 0)
2253 for (unsigned int f
= 0; f
< d2::image_rw::count(); f
++) {
2254 if (f
== f1
|| f
== f2
)
2257 pt _ptf
= align::projective(f
);
2258 _ptf
.scale(1 / _ptf
.scale_2d() / pow(2, ceil(input_decimation_exponent
)));
2260 point p
= _ptf
.wp_scaled(iw
);
2262 if (!cl
->reference
[f
]->in_bounds(p
.xy())
2263 || !if2
->in_bounds(ip
.xy()))
2266 divisor
+= tc_multiplier
;
2267 score
+= tc_multiplier
2268 * (if1
->get_bl(ip
.xy()) - cl
->reference
[f
]->get_bl(p
.xy())).normsq();
2271 *normalized_score
= score
/ divisor
;
2275 * Generate a map from scores to 3D points for various depths at point (i, j) in f1.
2277 static score_map
p2f_score_map(unsigned int i
, unsigned int j
, lod_image
*if1
, lod_image
*if2
,
2278 std::vector
<pt
> pt_outputs
) {
2283 * Map two depths to the secondary frame.
2286 point p1
= _pt2
.wp_scaled(_pt1
.pw_scaled(point(i
, j
, 1000)));
2287 point p2
= _pt2
.wp_scaled(_pt1
.pw_scaled(point(i
, j
, -1000)));
2290 * For cases where the mapped points define a
2291 * line and where points on the line fall
2292 * within the defined area of the frame,
2293 * determine the starting point for inspection.
2294 * In other cases, continue to the next pixel.
2297 ale_pos diff_i
= p2
[0] - p1
[0];
2298 ale_pos diff_j
= p2
[1] - p1
[1];
2299 ale_pos slope
= diff_j
/ diff_i
;
2302 fprintf(stderr
, "%d->%d (%d, %d) has undefined slope\n",
2308 * Make absurdly large/small slopes either infinity, negative infinity, or zero.
2311 if (fabs(slope
) > if2
->width() * 100) {
2314 double inf
= one
/ zero
;
2316 } else if (slope
< 1 / (double) if2
->height() / 100
2317 && slope
> -1/ (double) if2
->height() / 100) {
2321 ale_pos top_intersect
= p1
[1] - p1
[0] * slope
;
2322 ale_pos lef_intersect
= p1
[0] - p1
[1] / slope
;
2323 ale_pos rig_intersect
= p1
[0] - (p1
[1] - if2
->width() + 2) / slope
;
2327 sp_i
= lef_intersect
;
2329 } else if (finite(slope
) && top_intersect
>= 0 && top_intersect
< if2
->width() - 1) {
2331 sp_j
= top_intersect
;
2332 } else if (slope
> 0 && lef_intersect
>= 0 && lef_intersect
<= if2
->height() - 1) {
2333 sp_i
= lef_intersect
;
2335 } else if (slope
< 0 && rig_intersect
>= 0 && rig_intersect
<= if2
->height() - 1) {
2336 sp_i
= rig_intersect
;
2337 sp_j
= if2
->width() - 2;
2343 * Determine increment values for examining
2344 * point, ensuring that incr_i is always
2348 ale_pos incr_i
, incr_j
;
2350 if (fabs(diff_i
) > fabs(diff_j
)) {
2353 } else if (slope
> 0) {
2357 incr_i
= -1 / slope
;
2362 * Examine regions near the projected line.
2365 for (ale_pos ii
= sp_i
, jj
= sp_j
;
2366 ii
<= if2
->height() - 1 && jj
<= if2
->width() - 1 && ii
>= 0 && jj
>= 0;
2367 ii
+= incr_i
, jj
+= incr_j
) {
2369 ale_real normalized_score
;
2372 solve_point_pair(f1
, f2
, i
, j
, ii
, jj
, &normalized_score
, &_a
, _pt1
, _pt2
, if1
, if2
);
2375 * Reject points with undefined score.
2378 if (!finite(normalized_score
))
2382 * Add the point to the score map.
2385 result
.insert(score_map_element(normalized_score
, _a
));
2389 * Iterate through regions and add new locations with sub-pixel resolution
2391 int accumulated_passes
= 0;
2392 int max_acc_passes
= pairwise_ambiguity
;
2393 for (score_map::iterator smi
= result
.begin(); smi
!= result
.end(); smi
++) {
2394 point is
= smi
->second
.is
;
2396 if (accumulated_passes
++ >= max_acc_passes
)
2399 for (ale_pos epsilon
= -0.5; epsilon
<= 0.5; epsilon
+= 0.125) {
2401 if (fabs(epsilon
) < 0.001)
2404 ale_real normalized_score
;
2407 solve_point_pair(f1
, f2
, i
, j
, is
[0] + epsilon
* incr_i
, is
[1] + epsilon
* incr_j
,
2408 &normalized_score
, &_a
, _pt1
, _pt2
, if1
, if2
);
2410 if (!finite(normalized_score
))
2413 result
.insert(score_map_element(normalized_score
, _a
));
2423 static std::vector
<pt
> make_pt_list(const char *d_out
[], const char *v_out
[],
2424 std::map
<const char *, pt
> *d3_depth_pt
,
2425 std::map
<const char *, pt
> *d3_output_pt
) {
2427 std::vector
<pt
> result
;
2429 for (unsigned int n
= 0; n
< d2::image_rw::count(); n
++) {
2430 if (d_out
[n
] || v_out
[n
]) {
2431 result
.push_back(align::projective(n
));
2435 for (std::map
<const char *, pt
>::iterator i
= d3_depth_pt
->begin(); i
!= d3_depth_pt
->end(); i
++) {
2436 result
.push_back(i
->second
);
2439 for (std::map
<const char *, pt
>::iterator i
= d3_output_pt
->begin(); i
!= d3_output_pt
->end(); i
++) {
2440 result
.push_back(i
->second
);
2447 * Check whether a cell is visible from a given viewpoint. This function
2448 * is guaranteed to return 1 when a cell is visible, but it is not guaranteed
2449 * to return 0 when a cell is invisible.
2451 static int pt_might_be_visible(const pt
&viewpoint
, point min
, point max
) {
2453 point cell
[2] = {min
, max
};
2456 * Cycle through all vertices of the cell to check certain
2461 for (int i
= 0; i
< 2; i
++)
2462 for (int j
= 0; j
< 2; j
++)
2463 for (int k
= 0; k
< 2; k
++) {
2464 point p
= viewpoint
.wp_unscaled(point(cell
[i
][0], cell
[j
][1], cell
[k
][2]));
2465 if (viewpoint
.unscaled_in_bounds(p
))
2468 for (int d
= 0; d
< 3; d
++)
2472 if (p
[0] <= viewpoint
.unscaled_height() - 1)
2475 if (p
[1] <= viewpoint
.unscaled_width() - 1)
2485 if (pos
[2] && neg
[2])
2498 * Check whether a cell is output-visible.
2500 static void output_might_be_visible(const std::vector
<pt
> &pt_outputs
, point min
, point max
) {
2501 for (int n
= 0; n
< pt_outputs
.size(); n
++)
2502 if (pt_visible(pt_outputs
[n
], min
, max
))
2508 * Check whether a cell is input-visible.
2510 static void input_might_be_visible(unsigned int f
, point min
, point max
) {
2511 return pt_visible(align::projective(f
), min
, max
);
2515 * Check lower-bound resolution constraints
2517 int exceeds_resolution_lower_bounds(unsigned int f1
, unsigned int f2
,
2518 point min
, point max
, const std::vector
<pt
> &pt_outputs
) {
2523 * Try the candidate nearest to the specified cell.
2525 static void try_nearest_candidate(unsigned int f1
, unsigned int f2
, candidates
*c
, point min
, point max
) {
2529 int completely_clipped(point min
, point max
) {
2530 return (min
[2] > front_clip
2531 || max
[2] < rear_clip
)
2535 * Find candidates for subspace creation.
2537 static void find_candidates(unsigned int f1
, unsigned int f2
, candidates
*c
, point min
, point max
,
2538 const std::vector
<pt
> &pt_outputs
) {
2540 if (completely_clipped(min
, max
))
2543 if (!input_might_be_visible(f1
, min
, max
)
2544 || !input_might_be_visible(f2
, min
, max
))
2547 if (output_clip
&& !output_might_be_visible(pt_outputs
, min
, max
))
2550 if (exceeds_resolution_lower_bounds(f1
, f2
, min
, max
, pt_outputs
)) {
2551 try_nearest_candidate(f1
, f2
, c
, min
, max
);
2555 point new_cells
[2][2];
2557 space::traverse::get_next_cells(min
, max
, cells
);
2559 find_candidates(f1
, f2
, c
, new_cells
[0][0], new_cells
[0][1]);
2560 find_candidates(f1
, f2
, c
, new_cells
[1][0], new_cells
[1][1]);
2564 * Initialize space and identify regions of interest for the adaptive
2567 static void make_space(const char *d_out
[], const char *v_out
[],
2568 std::map
<const char *, pt
> *d3_depth_pt
,
2569 std::map
<const char *, pt
> *d3_output_pt
) {
2571 fprintf(stderr
, "Subdividing 3D space");
2573 std::vector
<pt
> pt_outputs
= make_pt_list(d_out
, v_out
, d3_depth_pt
, d3_output_pt
);
2576 * Initialize root space.
2582 * Subdivide space to resolve intensity matches between pairs
2586 for (unsigned int f1
= 0; f1
< d2::image_rw::count(); f1
++) {
2588 if (tc_multiplier
== 0)
2591 for (unsigned int f2
= 0; f2
< d2::image_rw::count(); f2
++) {
2596 if (tc_multiplier
== 0)
2599 candidates
*c
= new candidates(f1
);
2601 find_candidates(f1
, f2
, c
, point::neginf(), point::posinf(), pt_outputs
);
2603 c
->generate_subspaces();
2608 fprintf(stderr
, ".\n");
2613 * Update spatial information structures.
2615 * XXX: the name of this function is horribly misleading. There isn't
2616 * even a 'search depth' any longer, since there is no longer any
2617 * bounded DFS occurring.
2619 static void reduce_cost_to_search_depth(d2::exposure
*exp_out
, int inc_bit
) {
2625 for (unsigned int i
= 0; i
< ou_iterations
; i
++)
2626 spatial_info_update();
2631 * Describe a scene to a renderer
2633 static void describe(render
*r
) {