/src/harfbuzz/src/hb-ot-var-common.hh
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1 | | /* |
2 | | * Copyright © 2021 Google, Inc. |
3 | | * |
4 | | * This is part of HarfBuzz, a text shaping library. |
5 | | * |
6 | | * Permission is hereby granted, without written agreement and without |
7 | | * license or royalty fees, to use, copy, modify, and distribute this |
8 | | * software and its documentation for any purpose, provided that the |
9 | | * above copyright notice and the following two paragraphs appear in |
10 | | * all copies of this software. |
11 | | * |
12 | | * IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR |
13 | | * DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES |
14 | | * ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN |
15 | | * IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH |
16 | | * DAMAGE. |
17 | | * |
18 | | * THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, |
19 | | * BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND |
20 | | * FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS |
21 | | * ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO |
22 | | * PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. |
23 | | * |
24 | | */ |
25 | | |
26 | | #ifndef HB_OT_VAR_COMMON_HH |
27 | | #define HB_OT_VAR_COMMON_HH |
28 | | |
29 | | #include "hb-ot-layout-common.hh" |
30 | | #include "hb-alloc-pool.hh" |
31 | | #include "hb-priority-queue.hh" |
32 | | #include "hb-subset-instancer-iup.hh" |
33 | | |
34 | | |
35 | | namespace OT { |
36 | | |
37 | | using rebase_tent_result_scratch_t = hb_pair_t<rebase_tent_result_t, rebase_tent_result_t>; |
38 | | |
39 | | /* https://docs.microsoft.com/en-us/typography/opentype/spec/otvarcommonformats#tuplevariationheader */ |
40 | | struct TupleVariationHeader |
41 | | { |
42 | | friend struct tuple_delta_t; |
43 | | unsigned get_size (unsigned axis_count) const |
44 | 159k | { return min_size + get_all_tuples (axis_count).get_size (); } |
45 | | |
46 | 163k | unsigned get_data_size () const { return varDataSize; } |
47 | | |
48 | | const TupleVariationHeader &get_next (unsigned axis_count) const |
49 | 0 | { return StructAtOffset<TupleVariationHeader> (this, get_size (axis_count)); } |
50 | | |
51 | | bool unpack_axis_tuples (unsigned axis_count, |
52 | | const hb_array_t<const F2DOT14> shared_tuples, |
53 | | const hb_map_t *axes_old_index_tag_map, |
54 | | hb_hashmap_t<hb_tag_t, Triple>& axis_tuples /* OUT */) const |
55 | 0 | { |
56 | 0 | const F2DOT14 *peak_tuple = nullptr; |
57 | 0 | if (has_peak ()) |
58 | 0 | peak_tuple = get_peak_tuple (axis_count); |
59 | 0 | else |
60 | 0 | { |
61 | 0 | unsigned int index = get_index (); |
62 | 0 | if (unlikely ((index + 1) * axis_count > shared_tuples.length)) |
63 | 0 | return false; |
64 | 0 | peak_tuple = shared_tuples.sub_array (axis_count * index, axis_count).arrayZ; |
65 | 0 | } |
66 | 0 |
|
67 | 0 | const F2DOT14 *start_tuple = nullptr; |
68 | 0 | const F2DOT14 *end_tuple = nullptr; |
69 | 0 | bool has_interm = has_intermediate (); |
70 | 0 |
|
71 | 0 | if (has_interm) |
72 | 0 | { |
73 | 0 | start_tuple = get_start_tuple (axis_count); |
74 | 0 | end_tuple = get_end_tuple (axis_count); |
75 | 0 | } |
76 | 0 |
|
77 | 0 | for (unsigned i = 0; i < axis_count; i++) |
78 | 0 | { |
79 | 0 | float peak = peak_tuple[i].to_float (); |
80 | 0 | if (peak == 0.f) continue; |
81 | 0 |
|
82 | 0 | hb_tag_t *axis_tag; |
83 | 0 | if (!axes_old_index_tag_map->has (i, &axis_tag)) |
84 | 0 | return false; |
85 | 0 |
|
86 | 0 | float start, end; |
87 | 0 | if (has_interm) |
88 | 0 | { |
89 | 0 | start = start_tuple[i].to_float (); |
90 | 0 | end = end_tuple[i].to_float (); |
91 | 0 | } |
92 | 0 | else |
93 | 0 | { |
94 | 0 | start = hb_min (peak, 0.f); |
95 | 0 | end = hb_max (peak, 0.f); |
96 | 0 | } |
97 | 0 | axis_tuples.set (*axis_tag, Triple ((double) start, (double) peak, (double) end)); |
98 | 0 | } |
99 | 0 |
|
100 | 0 | return true; |
101 | 0 | } |
102 | | |
103 | | HB_ALWAYS_INLINE |
104 | | double calculate_scalar (hb_array_t<const int> coords, unsigned int coord_count, |
105 | | const hb_array_t<const F2DOT14> shared_tuples, |
106 | | hb_scalar_cache_t *shared_tuple_scalar_cache = nullptr) const |
107 | 156k | { |
108 | 156k | unsigned tuple_index = tupleIndex; |
109 | | |
110 | 156k | const F2DOT14 *peak_tuple; |
111 | | |
112 | 156k | bool has_interm = tuple_index & TuppleIndex::IntermediateRegion; // Inlined for performance |
113 | 156k | if (unlikely (has_interm)) |
114 | 29.8k | shared_tuple_scalar_cache = nullptr; |
115 | | |
116 | 156k | if (unlikely (tuple_index & TuppleIndex::EmbeddedPeakTuple)) // Inlined for performance |
117 | 25.1k | { |
118 | 25.1k | peak_tuple = get_peak_tuple (coord_count); |
119 | 25.1k | shared_tuple_scalar_cache = nullptr; |
120 | 25.1k | } |
121 | 131k | else |
122 | 131k | { |
123 | 131k | unsigned int index = tuple_index & TuppleIndex::TupleIndexMask; // Inlined for performance |
124 | | |
125 | 131k | float scalar; |
126 | 131k | if (shared_tuple_scalar_cache && |
127 | 60.4k | shared_tuple_scalar_cache->get (index, &scalar)) |
128 | 59.9k | return (double) scalar; |
129 | | |
130 | 71.8k | if (unlikely ((index + 1) * coord_count > shared_tuples.length)) |
131 | 38.5k | return 0.0; |
132 | 33.2k | peak_tuple = shared_tuples.arrayZ + (coord_count * index); |
133 | | |
134 | 33.2k | } |
135 | | |
136 | 58.3k | const F2DOT14 *start_tuple = nullptr; |
137 | 58.3k | const F2DOT14 *end_tuple = nullptr; |
138 | | |
139 | 58.3k | if (has_interm) |
140 | 18.4k | { |
141 | 18.4k | start_tuple = get_start_tuple (coord_count); |
142 | 18.4k | end_tuple = get_end_tuple (coord_count); |
143 | 18.4k | } |
144 | | |
145 | 58.3k | double scalar = 1.0; |
146 | 87.7k | for (unsigned int i = 0; i < coord_count; i++) |
147 | 65.2k | { |
148 | 65.2k | int peak = peak_tuple[i].to_int (); |
149 | 65.2k | if (!peak) continue; |
150 | | |
151 | 53.5k | int v = coords[i]; |
152 | 53.5k | if (!v) { scalar = 0.0; break; } |
153 | 44.3k | if (v == peak) continue; |
154 | | |
155 | 43.9k | if (has_interm) |
156 | 15.3k | { |
157 | 15.3k | int start = start_tuple[i].to_int (); |
158 | 15.3k | int end = end_tuple[i].to_int (); |
159 | 15.3k | if (unlikely (start > peak || peak > end || |
160 | 15.3k | (start < 0 && end > 0 && peak))) continue; |
161 | 6.06k | if (v < start || v > end) { scalar = 0.0; break; } |
162 | 1.75k | if (v < peak) |
163 | 500 | { if (peak != start) scalar *= (double) (v - start) / (peak - start); } |
164 | 1.25k | else |
165 | 1.25k | { if (peak != end) scalar *= (double) (end - v) / (end - peak); } |
166 | 1.75k | } |
167 | 28.5k | else if (v < hb_min (0, peak) || v > hb_max (0, peak)) { scalar = 0.0; break; } |
168 | 6.16k | else |
169 | 6.16k | scalar *= (double) v / peak; |
170 | 43.9k | } |
171 | 58.3k | if (shared_tuple_scalar_cache) |
172 | 158 | shared_tuple_scalar_cache->set (get_index (), scalar); |
173 | 58.3k | return scalar; |
174 | 156k | } |
175 | | |
176 | 196k | bool has_peak () const { return tupleIndex & TuppleIndex::EmbeddedPeakTuple; } |
177 | 159k | bool has_intermediate () const { return tupleIndex & TuppleIndex::IntermediateRegion; } |
178 | 14.3k | bool has_private_points () const { return tupleIndex & TuppleIndex::PrivatePointNumbers; } |
179 | 158 | unsigned get_index () const { return tupleIndex & TuppleIndex::TupleIndexMask; } |
180 | | |
181 | | protected: |
182 | | struct TuppleIndex : HBUINT16 |
183 | | { |
184 | | enum Flags { |
185 | | EmbeddedPeakTuple = 0x8000u, |
186 | | IntermediateRegion = 0x4000u, |
187 | | PrivatePointNumbers = 0x2000u, |
188 | | TupleIndexMask = 0x0FFFu |
189 | | }; |
190 | | |
191 | 0 | TuppleIndex& operator = (uint16_t i) { HBUINT16::operator= (i); return *this; } |
192 | | DEFINE_SIZE_STATIC (2); |
193 | | }; |
194 | | |
195 | | hb_array_t<const F2DOT14> get_all_tuples (unsigned axis_count) const |
196 | 159k | { return StructAfter<UnsizedArrayOf<F2DOT14>> (tupleIndex).as_array ((has_peak () + has_intermediate () * 2) * axis_count); } |
197 | | const F2DOT14* get_all_tuples_base (unsigned axis_count) const |
198 | 61.9k | { return StructAfter<UnsizedArrayOf<F2DOT14>> (tupleIndex).arrayZ; } |
199 | | const F2DOT14* get_peak_tuple (unsigned axis_count) const |
200 | 25.1k | { return get_all_tuples_base (axis_count); } |
201 | | const F2DOT14* get_start_tuple (unsigned axis_count) const |
202 | 18.4k | { return get_all_tuples_base (axis_count) + has_peak () * axis_count; } |
203 | | const F2DOT14* get_end_tuple (unsigned axis_count) const |
204 | 18.4k | { return get_all_tuples_base (axis_count) + has_peak () * axis_count + axis_count; } |
205 | | |
206 | | HBUINT16 varDataSize; /* The size in bytes of the serialized |
207 | | * data for this tuple variation table. */ |
208 | | TuppleIndex tupleIndex; /* A packed field. The high 4 bits are flags (see below). |
209 | | The low 12 bits are an index into a shared tuple |
210 | | records array. */ |
211 | | /* UnsizedArrayOf<F2DOT14> peakTuple - optional */ |
212 | | /* Peak tuple record for this tuple variation table — optional, |
213 | | * determined by flags in the tupleIndex value. |
214 | | * |
215 | | * Note that this must always be included in the 'cvar' table. */ |
216 | | /* UnsizedArrayOf<F2DOT14> intermediateStartTuple - optional */ |
217 | | /* Intermediate start tuple record for this tuple variation table — optional, |
218 | | determined by flags in the tupleIndex value. */ |
219 | | /* UnsizedArrayOf<F2DOT14> intermediateEndTuple - optional */ |
220 | | /* Intermediate end tuple record for this tuple variation table — optional, |
221 | | * determined by flags in the tupleIndex value. */ |
222 | | public: |
223 | | DEFINE_SIZE_MIN (4); |
224 | | }; |
225 | | |
226 | | struct optimize_scratch_t |
227 | | { |
228 | | iup_scratch_t iup; |
229 | | hb_vector_t<bool> opt_indices; |
230 | | hb_vector_t<int> rounded_x_deltas; |
231 | | hb_vector_t<int> rounded_y_deltas; |
232 | | hb_vector_t<float> opt_deltas_x; |
233 | | hb_vector_t<float> opt_deltas_y; |
234 | | hb_vector_t<unsigned char> opt_point_data; |
235 | | hb_vector_t<unsigned char> opt_deltas_data; |
236 | | hb_vector_t<unsigned char> point_data; |
237 | | hb_vector_t<unsigned char> deltas_data; |
238 | | hb_vector_t<int> rounded_deltas; |
239 | | }; |
240 | | |
241 | | struct tuple_delta_t |
242 | | { |
243 | | static constexpr bool realloc_move = true; // Watch out when adding new members! |
244 | | |
245 | | public: |
246 | | hb_hashmap_t<hb_tag_t, Triple> axis_tuples; |
247 | | |
248 | | /* indices_length = point_count, indice[i] = 1 means point i is referenced */ |
249 | | hb_vector_t<bool> indices; |
250 | | |
251 | | hb_vector_t<float> deltas_x; |
252 | | /* empty for cvar tuples */ |
253 | | hb_vector_t<float> deltas_y; |
254 | | |
255 | | /* compiled data: header and deltas |
256 | | * compiled point data is saved in a hashmap within tuple_variations_t cause |
257 | | * some point sets might be reused by different tuple variations */ |
258 | | hb_vector_t<unsigned char> compiled_tuple_header; |
259 | | hb_vector_t<unsigned char> compiled_deltas; |
260 | | |
261 | | hb_vector_t<F2DOT14> compiled_peak_coords; |
262 | | hb_vector_t<F2DOT14> compiled_interm_coords; |
263 | | |
264 | 0 | tuple_delta_t (hb_alloc_pool_t *pool = nullptr) {} |
265 | | tuple_delta_t (const tuple_delta_t& o) = default; |
266 | | tuple_delta_t& operator = (const tuple_delta_t& o) = default; |
267 | | |
268 | | friend void swap (tuple_delta_t& a, tuple_delta_t& b) noexcept |
269 | 0 | { |
270 | 0 | hb_swap (a.axis_tuples, b.axis_tuples); |
271 | 0 | hb_swap (a.indices, b.indices); |
272 | 0 | hb_swap (a.deltas_x, b.deltas_x); |
273 | 0 | hb_swap (a.deltas_y, b.deltas_y); |
274 | 0 | hb_swap (a.compiled_tuple_header, b.compiled_tuple_header); |
275 | 0 | hb_swap (a.compiled_deltas, b.compiled_deltas); |
276 | 0 | hb_swap (a.compiled_peak_coords, b.compiled_peak_coords); |
277 | 0 | } |
278 | | |
279 | | tuple_delta_t (tuple_delta_t&& o) noexcept : tuple_delta_t () |
280 | 0 | { hb_swap (*this, o); } |
281 | | |
282 | | tuple_delta_t& operator = (tuple_delta_t&& o) noexcept |
283 | 0 | { |
284 | 0 | hb_swap (*this, o); |
285 | 0 | return *this; |
286 | 0 | } |
287 | | |
288 | | void copy_from (const tuple_delta_t& o, hb_alloc_pool_t *pool = nullptr) |
289 | 0 | { |
290 | 0 | axis_tuples = o.axis_tuples; |
291 | 0 | indices.allocate_from_pool (pool, o.indices); |
292 | 0 | deltas_x.allocate_from_pool (pool, o.deltas_x); |
293 | 0 | deltas_y.allocate_from_pool (pool, o.deltas_y); |
294 | 0 | compiled_tuple_header.allocate_from_pool (pool, o.compiled_tuple_header); |
295 | 0 | compiled_deltas.allocate_from_pool (pool, o.compiled_deltas); |
296 | 0 | compiled_peak_coords.allocate_from_pool (pool, o.compiled_peak_coords); |
297 | 0 | compiled_interm_coords.allocate_from_pool (pool, o.compiled_interm_coords); |
298 | 0 | } |
299 | | |
300 | | void remove_axis (hb_tag_t axis_tag) |
301 | 0 | { axis_tuples.del (axis_tag); } |
302 | | |
303 | | bool set_tent (hb_tag_t axis_tag, Triple tent) |
304 | 0 | { return axis_tuples.set (axis_tag, tent); } |
305 | | |
306 | | tuple_delta_t& operator += (const tuple_delta_t& o) |
307 | 0 | { |
308 | 0 | unsigned num = indices.length; |
309 | 0 | for (unsigned i = 0; i < num; i++) |
310 | 0 | { |
311 | 0 | if (indices.arrayZ[i]) |
312 | 0 | { |
313 | 0 | if (o.indices.arrayZ[i]) |
314 | 0 | { |
315 | 0 | deltas_x[i] += o.deltas_x[i]; |
316 | 0 | if (deltas_y && o.deltas_y) |
317 | 0 | deltas_y[i] += o.deltas_y[i]; |
318 | 0 | } |
319 | 0 | } |
320 | 0 | else |
321 | 0 | { |
322 | 0 | if (!o.indices.arrayZ[i]) continue; |
323 | 0 | indices.arrayZ[i] = true; |
324 | 0 | deltas_x[i] = o.deltas_x[i]; |
325 | 0 | if (deltas_y && o.deltas_y) |
326 | 0 | deltas_y[i] = o.deltas_y[i]; |
327 | 0 | } |
328 | 0 | } |
329 | 0 | return *this; |
330 | 0 | } |
331 | | |
332 | | tuple_delta_t& operator *= (float scalar) |
333 | 0 | { |
334 | 0 | if (scalar == 1.0f) |
335 | 0 | return *this; |
336 | 0 |
|
337 | 0 | unsigned num = indices.length; |
338 | 0 | if (deltas_y) |
339 | 0 | for (unsigned i = 0; i < num; i++) |
340 | 0 | { |
341 | 0 | if (!indices.arrayZ[i]) continue; |
342 | 0 | deltas_x[i] *= scalar; |
343 | 0 | deltas_y[i] *= scalar; |
344 | 0 | } |
345 | 0 | else |
346 | 0 | for (unsigned i = 0; i < num; i++) |
347 | 0 | { |
348 | 0 | if (!indices.arrayZ[i]) continue; |
349 | 0 | deltas_x[i] *= scalar; |
350 | 0 | } |
351 | 0 | return *this; |
352 | 0 | } |
353 | | |
354 | | void change_tuple_var_axis_limit (hb_tag_t axis_tag, Triple axis_limit, |
355 | | TripleDistances axis_triple_distances, |
356 | | hb_vector_t<tuple_delta_t>& out, |
357 | | rebase_tent_result_scratch_t &scratch, |
358 | | hb_alloc_pool_t *pool = nullptr) |
359 | 0 | { |
360 | 0 | // May move *this out. |
361 | 0 |
|
362 | 0 | out.reset (); |
363 | 0 | Triple *tent; |
364 | 0 | if (!axis_tuples.has (axis_tag, &tent)) |
365 | 0 | { |
366 | 0 | out.push (std::move (*this)); |
367 | 0 | return; |
368 | 0 | } |
369 | 0 |
|
370 | 0 | if ((tent->minimum < 0.0 && tent->maximum > 0.0) || |
371 | 0 | !(tent->minimum <= tent->middle && tent->middle <= tent->maximum)) |
372 | 0 | return; |
373 | 0 |
|
374 | 0 | if (tent->middle == 0.0) |
375 | 0 | { |
376 | 0 | out.push (std::move (*this)); |
377 | 0 | return; |
378 | 0 | } |
379 | 0 |
|
380 | 0 | rebase_tent_result_t &solutions = scratch.first; |
381 | 0 | rebase_tent (*tent, axis_limit, axis_triple_distances, solutions, scratch.second); |
382 | 0 | for (unsigned i = 0; i < solutions.length; i++) |
383 | 0 | { |
384 | 0 | auto &t = solutions.arrayZ[i]; |
385 | 0 |
|
386 | 0 | tuple_delta_t new_var; |
387 | 0 | if (i < solutions.length - 1) |
388 | 0 | new_var.copy_from (*this, pool); |
389 | 0 | else |
390 | 0 | new_var = std::move (*this); |
391 | 0 |
|
392 | 0 | if (t.second == Triple ()) |
393 | 0 | new_var.remove_axis (axis_tag); |
394 | 0 | else |
395 | 0 | new_var.set_tent (axis_tag, t.second); |
396 | 0 |
|
397 | 0 | new_var *= t.first; |
398 | 0 | out.push (std::move (new_var)); |
399 | 0 | } |
400 | 0 | } |
401 | | |
402 | | bool compile_coords (const hb_map_t& axes_index_map, |
403 | | const hb_map_t& axes_old_index_tag_map, |
404 | | hb_alloc_pool_t *pool= nullptr) |
405 | 0 | { |
406 | 0 | unsigned cur_axis_count = axes_index_map.get_population (); |
407 | 0 | if (pool) |
408 | 0 | { |
409 | 0 | if (unlikely (!compiled_peak_coords.allocate_from_pool (pool, cur_axis_count))) |
410 | 0 | return false; |
411 | 0 | } |
412 | 0 | else if (unlikely (!compiled_peak_coords.resize (cur_axis_count))) |
413 | 0 | return false; |
414 | 0 |
|
415 | 0 | hb_array_t<F2DOT14> start_coords, end_coords; |
416 | 0 |
|
417 | 0 | unsigned orig_axis_count = axes_old_index_tag_map.get_population (); |
418 | 0 | unsigned j = 0; |
419 | 0 | for (unsigned i = 0; i < orig_axis_count; i++) |
420 | 0 | { |
421 | 0 | if (!axes_index_map.has (i)) |
422 | 0 | continue; |
423 | 0 |
|
424 | 0 | hb_tag_t axis_tag = axes_old_index_tag_map.get (i); |
425 | 0 | Triple *coords = nullptr; |
426 | 0 | if (axis_tuples.has (axis_tag, &coords)) |
427 | 0 | { |
428 | 0 | float min_val = coords->minimum; |
429 | 0 | float val = coords->middle; |
430 | 0 | float max_val = coords->maximum; |
431 | 0 |
|
432 | 0 | compiled_peak_coords.arrayZ[j].set_float (val); |
433 | 0 |
|
434 | 0 | if (min_val != hb_min (val, 0.f) || max_val != hb_max (val, 0.f)) |
435 | 0 | { |
436 | 0 | if (!compiled_interm_coords) |
437 | 0 | { |
438 | 0 | if (pool) |
439 | 0 | { |
440 | 0 | if (unlikely (!compiled_interm_coords.allocate_from_pool (pool, 2 * cur_axis_count))) |
441 | 0 | return false; |
442 | 0 | } |
443 | 0 | else if (unlikely (!compiled_interm_coords.resize (2 * cur_axis_count))) |
444 | 0 | return false; |
445 | 0 | start_coords = compiled_interm_coords.as_array ().sub_array (0, cur_axis_count); |
446 | 0 | end_coords = compiled_interm_coords.as_array ().sub_array (cur_axis_count); |
447 | 0 |
|
448 | 0 | for (unsigned k = 0; k < j; k++) |
449 | 0 | { |
450 | 0 | signed peak = compiled_peak_coords.arrayZ[k].to_int (); |
451 | 0 | if (!peak) continue; |
452 | 0 | start_coords.arrayZ[k].set_int (hb_min (peak, 0)); |
453 | 0 | end_coords.arrayZ[k].set_int (hb_max (peak, 0)); |
454 | 0 | } |
455 | 0 | } |
456 | 0 |
|
457 | 0 | } |
458 | 0 |
|
459 | 0 | if (compiled_interm_coords) |
460 | 0 | { |
461 | 0 | start_coords.arrayZ[j].set_float (min_val); |
462 | 0 | end_coords.arrayZ[j].set_float (max_val); |
463 | 0 | } |
464 | 0 | } |
465 | 0 |
|
466 | 0 | j++; |
467 | 0 | } |
468 | 0 |
|
469 | 0 | return !compiled_peak_coords.in_error () && !compiled_interm_coords.in_error (); |
470 | 0 | } |
471 | | |
472 | | /* deltas should be compiled already before we compile tuple |
473 | | * variation header cause we need to fill in the size of the |
474 | | * serialized data for this tuple variation */ |
475 | | bool compile_tuple_var_header (const hb_map_t& axes_index_map, |
476 | | unsigned points_data_length, |
477 | | const hb_map_t& axes_old_index_tag_map, |
478 | | const hb_hashmap_t<const hb_vector_t<F2DOT14>*, unsigned>* shared_tuples_idx_map, |
479 | | hb_alloc_pool_t *pool = nullptr) |
480 | 0 | { |
481 | 0 | /* compiled_deltas could be empty after iup delta optimization, we can skip |
482 | 0 | * compiling this tuple and return true */ |
483 | 0 | if (!compiled_deltas) return true; |
484 | 0 |
|
485 | 0 | unsigned cur_axis_count = axes_index_map.get_population (); |
486 | 0 | /* allocate enough memory: 1 peak + 2 intermediate coords + fixed header size */ |
487 | 0 | unsigned alloc_len = 3 * cur_axis_count * (F2DOT14::static_size) + 4; |
488 | 0 | if (unlikely (!compiled_tuple_header.allocate_from_pool (pool, alloc_len, false))) return false; |
489 | 0 |
|
490 | 0 | unsigned flag = 0; |
491 | 0 | /* skip the first 4 header bytes: variationDataSize+tupleIndex */ |
492 | 0 | F2DOT14* p = reinterpret_cast<F2DOT14 *> (compiled_tuple_header.begin () + 4); |
493 | 0 | F2DOT14* end = reinterpret_cast<F2DOT14 *> (compiled_tuple_header.end ()); |
494 | 0 | hb_array_t<F2DOT14> coords (p, end - p); |
495 | 0 |
|
496 | 0 | if (!shared_tuples_idx_map) |
497 | 0 | compile_coords (axes_index_map, axes_old_index_tag_map); // non-gvar tuples do not have compiled coords yet |
498 | 0 |
|
499 | 0 | /* encode peak coords */ |
500 | 0 | unsigned peak_count = 0; |
501 | 0 | unsigned *shared_tuple_idx; |
502 | 0 | if (shared_tuples_idx_map && |
503 | 0 | shared_tuples_idx_map->has (&compiled_peak_coords, &shared_tuple_idx)) |
504 | 0 | { |
505 | 0 | flag = *shared_tuple_idx; |
506 | 0 | } |
507 | 0 | else |
508 | 0 | { |
509 | 0 | peak_count = encode_peak_coords(coords, flag); |
510 | 0 | if (!peak_count) return false; |
511 | 0 | } |
512 | 0 |
|
513 | 0 | /* encode interim coords, it's optional so returned num could be 0 */ |
514 | 0 | unsigned interim_count = encode_interm_coords (coords.sub_array (peak_count), flag); |
515 | 0 |
|
516 | 0 | /* pointdata length = 0 implies "use shared points" */ |
517 | 0 | if (points_data_length) |
518 | 0 | flag |= TupleVariationHeader::TuppleIndex::PrivatePointNumbers; |
519 | 0 |
|
520 | 0 | unsigned serialized_data_size = points_data_length + compiled_deltas.length; |
521 | 0 | TupleVariationHeader *o = reinterpret_cast<TupleVariationHeader *> (compiled_tuple_header.begin ()); |
522 | 0 | o->varDataSize = serialized_data_size; |
523 | 0 | o->tupleIndex = flag; |
524 | 0 |
|
525 | 0 | unsigned total_header_len = 4 + (peak_count + interim_count) * (F2DOT14::static_size); |
526 | 0 | compiled_tuple_header.shrink_back_to_pool (pool, total_header_len); |
527 | 0 | return true; |
528 | 0 | } |
529 | | |
530 | | unsigned encode_peak_coords (hb_array_t<F2DOT14> peak_coords, |
531 | | unsigned& flag) const |
532 | 0 | { |
533 | 0 | hb_memcpy (&peak_coords[0], &compiled_peak_coords[0], compiled_peak_coords.length * sizeof (compiled_peak_coords[0])); |
534 | 0 | flag |= TupleVariationHeader::TuppleIndex::EmbeddedPeakTuple; |
535 | 0 | return compiled_peak_coords.length; |
536 | 0 | } |
537 | | |
538 | | /* if no need to encode intermediate coords, then just return p */ |
539 | | unsigned encode_interm_coords (hb_array_t<F2DOT14> coords, |
540 | | unsigned& flag) const |
541 | 0 | { |
542 | 0 | if (compiled_interm_coords) |
543 | 0 | { |
544 | 0 | hb_memcpy (&coords[0], &compiled_interm_coords[0], compiled_interm_coords.length * sizeof (compiled_interm_coords[0])); |
545 | 0 | flag |= TupleVariationHeader::TuppleIndex::IntermediateRegion; |
546 | 0 | } |
547 | 0 | return compiled_interm_coords.length; |
548 | 0 | } |
549 | | |
550 | | bool compile_deltas (hb_vector_t<int> &rounded_deltas_scratch, |
551 | | hb_alloc_pool_t *pool = nullptr) |
552 | 0 | { return compile_deltas (indices, deltas_x, deltas_y, compiled_deltas, rounded_deltas_scratch, pool); } |
553 | | |
554 | | static bool compile_deltas (hb_array_t<const bool> point_indices, |
555 | | hb_array_t<const float> x_deltas, |
556 | | hb_array_t<const float> y_deltas, |
557 | | hb_vector_t<unsigned char> &compiled_deltas, /* OUT */ |
558 | | hb_vector_t<int> &rounded_deltas, /* scratch */ |
559 | | hb_alloc_pool_t *pool = nullptr) |
560 | 0 | { |
561 | 0 | if (unlikely (!rounded_deltas.resize_dirty (point_indices.length))) |
562 | 0 | return false; |
563 | 0 |
|
564 | 0 | unsigned j = 0; |
565 | 0 | for (unsigned i = 0; i < point_indices.length; i++) |
566 | 0 | { |
567 | 0 | if (!point_indices[i]) continue; |
568 | 0 | rounded_deltas.arrayZ[j++] = (int) roundf (x_deltas.arrayZ[i]); |
569 | 0 | } |
570 | 0 | rounded_deltas.resize (j); |
571 | 0 |
|
572 | 0 | if (!rounded_deltas) return true; |
573 | 0 | /* Allocate enough memory: this is the correct bound: |
574 | 0 | * Worst case scenario is that each delta has to be encoded in 4 bytes, and there |
575 | 0 | * are runs of 64 items each. Any delta encoded in less than 4 bytes (2, 1, or 0) |
576 | 0 | * is still smaller than the 4-byte encoding even with their control byte. |
577 | 0 | * The initial 2 is to handle length==0, for both x and y deltas. */ |
578 | 0 | unsigned alloc_len = 2 + 4 * rounded_deltas.length + (rounded_deltas.length + 63) / 64; |
579 | 0 | if (y_deltas) |
580 | 0 | alloc_len *= 2; |
581 | 0 |
|
582 | 0 | if (unlikely (!compiled_deltas.allocate_from_pool (pool, alloc_len, false))) return false; |
583 | 0 |
|
584 | 0 | unsigned encoded_len = compile_deltas (compiled_deltas, rounded_deltas); |
585 | 0 |
|
586 | 0 | if (y_deltas) |
587 | 0 | { |
588 | 0 | /* reuse the rounded_deltas vector, check that y_deltas have the same num of deltas as x_deltas */ |
589 | 0 | unsigned j = 0; |
590 | 0 | for (unsigned idx = 0; idx < point_indices.length; idx++) |
591 | 0 | { |
592 | 0 | if (!point_indices[idx]) continue; |
593 | 0 | int rounded_delta = (int) roundf (y_deltas.arrayZ[idx]); |
594 | 0 |
|
595 | 0 | if (j >= rounded_deltas.length) return false; |
596 | 0 |
|
597 | 0 | rounded_deltas[j++] = rounded_delta; |
598 | 0 | } |
599 | 0 |
|
600 | 0 | if (j != rounded_deltas.length) return false; |
601 | 0 | encoded_len += compile_deltas (compiled_deltas.as_array ().sub_array (encoded_len), rounded_deltas); |
602 | 0 | } |
603 | 0 | compiled_deltas.shrink_back_to_pool (pool, encoded_len); |
604 | 0 | return true; |
605 | 0 | } |
606 | | |
607 | | static unsigned compile_deltas (hb_array_t<unsigned char> encoded_bytes, |
608 | | hb_array_t<const int> deltas) |
609 | 0 | { |
610 | 0 | return TupleValues::compile_unsafe (deltas, encoded_bytes); |
611 | 0 | } |
612 | | |
613 | | bool calc_inferred_deltas (const contour_point_vector_t& orig_points, |
614 | | hb_vector_t<unsigned> &scratch) |
615 | 0 | { |
616 | 0 | unsigned point_count = orig_points.length; |
617 | 0 | if (point_count != indices.length) |
618 | 0 | return false; |
619 | 0 |
|
620 | 0 | unsigned ref_count = 0; |
621 | 0 |
|
622 | 0 | hb_vector_t<unsigned> &end_points = scratch.reset (); |
623 | 0 |
|
624 | 0 | for (unsigned i = 0; i < point_count; i++) |
625 | 0 | { |
626 | 0 | ref_count += indices.arrayZ[i]; |
627 | 0 | if (orig_points.arrayZ[i].is_end_point) |
628 | 0 | end_points.push (i); |
629 | 0 | } |
630 | 0 | /* all points are referenced, nothing to do */ |
631 | 0 | if (ref_count == point_count) |
632 | 0 | return true; |
633 | 0 | if (unlikely (end_points.in_error ())) return false; |
634 | 0 |
|
635 | 0 | hb_bit_set_t inferred_idxes; |
636 | 0 | unsigned start_point = 0; |
637 | 0 | for (unsigned end_point : end_points) |
638 | 0 | { |
639 | 0 | /* Check the number of unreferenced points in a contour. If no unref points or no ref points, nothing to do. */ |
640 | 0 | unsigned unref_count = 0; |
641 | 0 | for (unsigned i = start_point; i < end_point + 1; i++) |
642 | 0 | unref_count += indices.arrayZ[i]; |
643 | 0 | unref_count = (end_point - start_point + 1) - unref_count; |
644 | 0 |
|
645 | 0 | unsigned j = start_point; |
646 | 0 | if (unref_count == 0 || unref_count > end_point - start_point) |
647 | 0 | goto no_more_gaps; |
648 | 0 | for (;;) |
649 | 0 | { |
650 | 0 | /* Locate the next gap of unreferenced points between two referenced points prev and next. |
651 | 0 | * Note that a gap may wrap around at left (start_point) and/or at right (end_point). |
652 | 0 | */ |
653 | 0 | unsigned int prev, next, i; |
654 | 0 | for (;;) |
655 | 0 | { |
656 | 0 | i = j; |
657 | 0 | j = next_index (i, start_point, end_point); |
658 | 0 | if (indices.arrayZ[i] && !indices.arrayZ[j]) break; |
659 | 0 | } |
660 | 0 | prev = j = i; |
661 | 0 | for (;;) |
662 | 0 | { |
663 | 0 | i = j; |
664 | 0 | j = next_index (i, start_point, end_point); |
665 | 0 | if (!indices.arrayZ[i] && indices.arrayZ[j]) break; |
666 | 0 | } |
667 | 0 | next = j; |
668 | 0 | /* Infer deltas for all unref points in the gap between prev and next */ |
669 | 0 | i = prev; |
670 | 0 | for (;;) |
671 | 0 | { |
672 | 0 | i = next_index (i, start_point, end_point); |
673 | 0 | if (i == next) break; |
674 | 0 | deltas_x.arrayZ[i] = infer_delta ((double) orig_points.arrayZ[i].x, |
675 | 0 | (double) orig_points.arrayZ[prev].x, |
676 | 0 | (double) orig_points.arrayZ[next].x, |
677 | 0 | (double) deltas_x.arrayZ[prev], (double) deltas_x.arrayZ[next]); |
678 | 0 | deltas_y.arrayZ[i] = infer_delta ((double) orig_points.arrayZ[i].y, |
679 | 0 | (double) orig_points.arrayZ[prev].y, |
680 | 0 | (double) orig_points.arrayZ[next].y, |
681 | 0 | (double) deltas_y.arrayZ[prev], (double) deltas_y.arrayZ[next]); |
682 | 0 | inferred_idxes.add (i); |
683 | 0 | if (--unref_count == 0) goto no_more_gaps; |
684 | 0 | } |
685 | 0 | } |
686 | 0 | no_more_gaps: |
687 | 0 | start_point = end_point + 1; |
688 | 0 | } |
689 | 0 |
|
690 | 0 | for (unsigned i = 0; i < point_count; i++) |
691 | 0 | { |
692 | 0 | /* if points are not referenced and deltas are not inferred, set to 0. |
693 | 0 | * reference all points for gvar */ |
694 | 0 | if ( !indices[i]) |
695 | 0 | { |
696 | 0 | if (!inferred_idxes.has (i)) |
697 | 0 | { |
698 | 0 | deltas_x.arrayZ[i] = 0.0; |
699 | 0 | deltas_y.arrayZ[i] = 0.0; |
700 | 0 | } |
701 | 0 | indices[i] = true; |
702 | 0 | } |
703 | 0 | } |
704 | 0 | return true; |
705 | 0 | } |
706 | | |
707 | | bool optimize (const contour_point_vector_t& contour_points, |
708 | | bool is_composite, |
709 | | optimize_scratch_t &scratch, |
710 | | double tolerance = 0.5 + 1e-10) |
711 | 0 | { |
712 | 0 | unsigned count = contour_points.length; |
713 | 0 | if (deltas_x.length != count || |
714 | 0 | deltas_y.length != count) |
715 | 0 | return false; |
716 | 0 |
|
717 | 0 | hb_vector_t<bool> &opt_indices = scratch.opt_indices.reset (); |
718 | 0 | hb_vector_t<int> &rounded_x_deltas = scratch.rounded_x_deltas; |
719 | 0 | hb_vector_t<int> &rounded_y_deltas = scratch.rounded_y_deltas; |
720 | 0 |
|
721 | 0 | if (unlikely (!rounded_x_deltas.resize_dirty (count) || |
722 | 0 | !rounded_y_deltas.resize_dirty (count))) |
723 | 0 | return false; |
724 | 0 |
|
725 | 0 | for (unsigned i = 0; i < count; i++) |
726 | 0 | { |
727 | 0 | rounded_x_deltas.arrayZ[i] = (int) roundf (deltas_x.arrayZ[i]); |
728 | 0 | rounded_y_deltas.arrayZ[i] = (int) roundf (deltas_y.arrayZ[i]); |
729 | 0 | } |
730 | 0 |
|
731 | 0 | if (!iup_delta_optimize (contour_points, rounded_x_deltas, rounded_y_deltas, opt_indices, scratch.iup, tolerance)) |
732 | 0 | return false; |
733 | 0 |
|
734 | 0 | unsigned ref_count = 0; |
735 | 0 | for (bool ref_flag : opt_indices) |
736 | 0 | ref_count += ref_flag; |
737 | 0 |
|
738 | 0 | if (ref_count == count) return true; |
739 | 0 |
|
740 | 0 | hb_vector_t<float> &opt_deltas_x = scratch.opt_deltas_x.reset (); |
741 | 0 | hb_vector_t<float> &opt_deltas_y = scratch.opt_deltas_y.reset (); |
742 | 0 | bool is_comp_glyph_wo_deltas = (is_composite && ref_count == 0); |
743 | 0 | if (is_comp_glyph_wo_deltas) |
744 | 0 | { |
745 | 0 | if (unlikely (!opt_deltas_x.resize (count) || |
746 | 0 | !opt_deltas_y.resize (count))) |
747 | 0 | return false; |
748 | 0 |
|
749 | 0 | opt_indices.arrayZ[0] = true; |
750 | 0 | for (unsigned i = 1; i < count; i++) |
751 | 0 | opt_indices.arrayZ[i] = false; |
752 | 0 | } |
753 | 0 |
|
754 | 0 | hb_vector_t<unsigned char> &opt_point_data = scratch.opt_point_data.reset (); |
755 | 0 | if (!compile_point_set (opt_indices, opt_point_data)) |
756 | 0 | return false; |
757 | 0 | hb_vector_t<unsigned char> &opt_deltas_data = scratch.opt_deltas_data.reset (); |
758 | 0 | if (!compile_deltas (opt_indices, |
759 | 0 | is_comp_glyph_wo_deltas ? opt_deltas_x : deltas_x, |
760 | 0 | is_comp_glyph_wo_deltas ? opt_deltas_y : deltas_y, |
761 | 0 | opt_deltas_data, |
762 | 0 | scratch.rounded_deltas)) |
763 | 0 | return false; |
764 | 0 |
|
765 | 0 | hb_vector_t<unsigned char> &point_data = scratch.point_data.reset (); |
766 | 0 | if (!compile_point_set (indices, point_data)) |
767 | 0 | return false; |
768 | 0 | hb_vector_t<unsigned char> &deltas_data = scratch.deltas_data.reset (); |
769 | 0 | if (!compile_deltas (indices, deltas_x, deltas_y, deltas_data, scratch.rounded_deltas)) |
770 | 0 | return false; |
771 | 0 |
|
772 | 0 | if (opt_point_data.length + opt_deltas_data.length < point_data.length + deltas_data.length) |
773 | 0 | { |
774 | 0 | indices = std::move (opt_indices); |
775 | 0 |
|
776 | 0 | if (is_comp_glyph_wo_deltas) |
777 | 0 | { |
778 | 0 | deltas_x = std::move (opt_deltas_x); |
779 | 0 | deltas_y = std::move (opt_deltas_y); |
780 | 0 | } |
781 | 0 | } |
782 | 0 | return !indices.in_error () && !deltas_x.in_error () && !deltas_y.in_error (); |
783 | 0 | } |
784 | | |
785 | | static bool compile_point_set (const hb_vector_t<bool> &point_indices, |
786 | | hb_vector_t<unsigned char>& compiled_points /* OUT */) |
787 | 0 | { |
788 | 0 | unsigned num_points = 0; |
789 | 0 | for (bool i : point_indices) |
790 | 0 | if (i) num_points++; |
791 | 0 |
|
792 | 0 | /* when iup optimization is enabled, num of referenced points could be 0 */ |
793 | 0 | if (!num_points) return true; |
794 | 0 |
|
795 | 0 | unsigned indices_length = point_indices.length; |
796 | 0 | /* If the points set consists of all points in the glyph, it's encoded with a |
797 | 0 | * single zero byte */ |
798 | 0 | if (num_points == indices_length) |
799 | 0 | return compiled_points.resize (1); |
800 | 0 |
|
801 | 0 | /* allocate enough memories: 2 bytes for count + 3 bytes for each point */ |
802 | 0 | unsigned num_bytes = 2 + 3 *num_points; |
803 | 0 | if (unlikely (!compiled_points.resize_dirty (num_bytes))) |
804 | 0 | return false; |
805 | 0 |
|
806 | 0 | unsigned pos = 0; |
807 | 0 | /* binary data starts with the total number of reference points */ |
808 | 0 | if (num_points < 0x80) |
809 | 0 | compiled_points.arrayZ[pos++] = num_points; |
810 | 0 | else |
811 | 0 | { |
812 | 0 | compiled_points.arrayZ[pos++] = ((num_points >> 8) | 0x80); |
813 | 0 | compiled_points.arrayZ[pos++] = num_points & 0xFF; |
814 | 0 | } |
815 | 0 |
|
816 | 0 | const unsigned max_run_length = 0x7F; |
817 | 0 | unsigned i = 0; |
818 | 0 | unsigned last_value = 0; |
819 | 0 | unsigned num_encoded = 0; |
820 | 0 | while (i < indices_length && num_encoded < num_points) |
821 | 0 | { |
822 | 0 | unsigned run_length = 0; |
823 | 0 | unsigned header_pos = pos; |
824 | 0 | compiled_points.arrayZ[pos++] = 0; |
825 | 0 |
|
826 | 0 | bool use_byte_encoding = false; |
827 | 0 | bool new_run = true; |
828 | 0 | while (i < indices_length && num_encoded < num_points && |
829 | 0 | run_length <= max_run_length) |
830 | 0 | { |
831 | 0 | // find out next referenced point index |
832 | 0 | while (i < indices_length && !point_indices[i]) |
833 | 0 | i++; |
834 | 0 |
|
835 | 0 | if (i >= indices_length) break; |
836 | 0 |
|
837 | 0 | unsigned cur_value = i; |
838 | 0 | unsigned delta = cur_value - last_value; |
839 | 0 |
|
840 | 0 | if (new_run) |
841 | 0 | { |
842 | 0 | use_byte_encoding = (delta <= 0xFF); |
843 | 0 | new_run = false; |
844 | 0 | } |
845 | 0 |
|
846 | 0 | if (use_byte_encoding && delta > 0xFF) |
847 | 0 | break; |
848 | 0 |
|
849 | 0 | if (use_byte_encoding) |
850 | 0 | compiled_points.arrayZ[pos++] = delta; |
851 | 0 | else |
852 | 0 | { |
853 | 0 | compiled_points.arrayZ[pos++] = delta >> 8; |
854 | 0 | compiled_points.arrayZ[pos++] = delta & 0xFF; |
855 | 0 | } |
856 | 0 | i++; |
857 | 0 | last_value = cur_value; |
858 | 0 | run_length++; |
859 | 0 | num_encoded++; |
860 | 0 | } |
861 | 0 |
|
862 | 0 | if (use_byte_encoding) |
863 | 0 | compiled_points.arrayZ[header_pos] = run_length - 1; |
864 | 0 | else |
865 | 0 | compiled_points.arrayZ[header_pos] = (run_length - 1) | 0x80; |
866 | 0 | } |
867 | 0 | return compiled_points.resize_dirty (pos); |
868 | 0 | } |
869 | | |
870 | | static double infer_delta (double target_val, double prev_val, double next_val, double prev_delta, double next_delta) |
871 | 0 | { |
872 | 0 | if (prev_val == next_val) |
873 | 0 | return (prev_delta == next_delta) ? prev_delta : 0.0; |
874 | 0 | else if (target_val <= hb_min (prev_val, next_val)) |
875 | 0 | return (prev_val < next_val) ? prev_delta : next_delta; |
876 | 0 | else if (target_val >= hb_max (prev_val, next_val)) |
877 | 0 | return (prev_val > next_val) ? prev_delta : next_delta; |
878 | 0 |
|
879 | 0 | double r = (target_val - prev_val) / (next_val - prev_val); |
880 | 0 | return prev_delta + r * (next_delta - prev_delta); |
881 | 0 | } |
882 | | |
883 | | static unsigned int next_index (unsigned int i, unsigned int start, unsigned int end) |
884 | 0 | { return (i >= end) ? start : (i + 1); } |
885 | | }; |
886 | | |
887 | | template <typename OffType = HBUINT16> |
888 | | struct TupleVariationData |
889 | | { |
890 | | bool sanitize (hb_sanitize_context_t *c) const |
891 | | { |
892 | | TRACE_SANITIZE (this); |
893 | | // here check on min_size only, TupleVariationHeader and var data will be |
894 | | // checked while accessing through iterator. |
895 | | return_trace (c->check_struct (this)); |
896 | | } |
897 | | |
898 | | unsigned get_size (unsigned axis_count) const |
899 | | { |
900 | | unsigned total_size = min_size; |
901 | | unsigned count = tupleVarCount.get_count (); |
902 | | const TupleVariationHeader *tuple_var_header = &(get_tuple_var_header()); |
903 | | for (unsigned i = 0; i < count; i++) |
904 | | { |
905 | | total_size += tuple_var_header->get_size (axis_count) + tuple_var_header->get_data_size (); |
906 | | tuple_var_header = &tuple_var_header->get_next (axis_count); |
907 | | } |
908 | | |
909 | | return total_size; |
910 | | } |
911 | | |
912 | | const TupleVariationHeader &get_tuple_var_header (void) const |
913 | 32.6k | { return StructAfter<TupleVariationHeader> (data); } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::get_tuple_var_header() const Line | Count | Source | 913 | 15.7k | { return StructAfter<TupleVariationHeader> (data); } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::get_tuple_var_header() const Line | Count | Source | 913 | 16.9k | { return StructAfter<TupleVariationHeader> (data); } |
|
914 | | |
915 | | struct tuple_iterator_t; |
916 | | struct tuple_variations_t |
917 | | { |
918 | | hb_vector_t<tuple_delta_t> tuple_vars; |
919 | | |
920 | | private: |
921 | | /* referenced point set->compiled point data map */ |
922 | | hb_hashmap_t<const hb_vector_t<bool>*, hb_vector_t<unsigned char>> point_data_map; |
923 | | /* referenced point set-> count map, used in finding shared points */ |
924 | | hb_hashmap_t<const hb_vector_t<bool>*, unsigned> point_set_count_map; |
925 | | |
926 | | /* empty for non-gvar tuples. |
927 | | * shared_points_bytes is a pointer to some value in the point_data_map, |
928 | | * which will be freed during map destruction. Save it for serialization, so |
929 | | * no need to do find_shared_points () again */ |
930 | | hb_vector_t<unsigned char> *shared_points_bytes = nullptr; |
931 | | |
932 | | /* total compiled byte size as TupleVariationData format, initialized to 0 */ |
933 | | unsigned compiled_byte_size = 0; |
934 | | bool needs_padding = false; |
935 | | |
936 | | /* for gvar iup delta optimization: whether this is a composite glyph */ |
937 | | bool is_composite = false; |
938 | | |
939 | | public: |
940 | | tuple_variations_t () = default; |
941 | | tuple_variations_t (const tuple_variations_t&) = delete; |
942 | | tuple_variations_t& operator=(const tuple_variations_t&) = delete; |
943 | | tuple_variations_t (tuple_variations_t&&) = default; |
944 | | tuple_variations_t& operator=(tuple_variations_t&&) = default; |
945 | | ~tuple_variations_t () = default; |
946 | | |
947 | | explicit operator bool () const { return bool (tuple_vars); } |
948 | | unsigned get_var_count () const |
949 | | { |
950 | | unsigned count = 0; |
951 | | /* when iup delta opt is enabled, compiled_deltas could be empty and we |
952 | | * should skip this tuple */ |
953 | | for (auto& tuple: tuple_vars) |
954 | | if (tuple.compiled_deltas) count++; |
955 | | |
956 | | if (shared_points_bytes && shared_points_bytes->length) |
957 | | count |= TupleVarCount::SharedPointNumbers; |
958 | | return count; |
959 | | } |
960 | | |
961 | | unsigned get_compiled_byte_size () const |
962 | | { return compiled_byte_size; } |
963 | | |
964 | | bool create_from_tuple_var_data (tuple_iterator_t iterator, |
965 | | unsigned tuple_var_count, |
966 | | unsigned point_count, |
967 | | bool is_gvar, |
968 | | const hb_map_t *axes_old_index_tag_map, |
969 | | const hb_vector_t<unsigned> &shared_indices, |
970 | | const hb_array_t<const F2DOT14> shared_tuples, |
971 | | hb_alloc_pool_t *pool = nullptr, |
972 | | bool is_composite_glyph = false) |
973 | | { |
974 | | hb_vector_t<unsigned> private_indices; |
975 | | hb_vector_t<int> deltas_x; |
976 | | hb_vector_t<int> deltas_y; |
977 | | do |
978 | | { |
979 | | const HBUINT8 *p = iterator.get_serialized_data (); |
980 | | unsigned int length = iterator.current_tuple->get_data_size (); |
981 | | if (unlikely (!iterator.var_data_bytes.check_range (p, length))) |
982 | | return false; |
983 | | |
984 | | hb_hashmap_t<hb_tag_t, Triple> axis_tuples; |
985 | | if (!iterator.current_tuple->unpack_axis_tuples (iterator.get_axis_count (), shared_tuples, axes_old_index_tag_map, axis_tuples) |
986 | | || axis_tuples.is_empty ()) |
987 | | return false; |
988 | | |
989 | | private_indices.reset (); |
990 | | bool has_private_points = iterator.current_tuple->has_private_points (); |
991 | | const HBUINT8 *end = p + length; |
992 | | if (has_private_points && |
993 | | !TupleVariationData::decompile_points (p, private_indices, end)) |
994 | | return false; |
995 | | |
996 | | const hb_vector_t<unsigned> &indices = has_private_points ? private_indices : shared_indices; |
997 | | bool apply_to_all = (indices.length == 0); |
998 | | unsigned num_deltas = apply_to_all ? point_count : indices.length; |
999 | | |
1000 | | if (unlikely (!deltas_x.resize_dirty (num_deltas) || |
1001 | | !TupleVariationData::decompile_deltas (p, deltas_x, end))) |
1002 | | return false; |
1003 | | |
1004 | | if (is_gvar) |
1005 | | { |
1006 | | if (unlikely (!deltas_y.resize_dirty (num_deltas) || |
1007 | | !TupleVariationData::decompile_deltas (p, deltas_y, end))) |
1008 | | return false; |
1009 | | } |
1010 | | |
1011 | | tuple_delta_t var; |
1012 | | var.axis_tuples = std::move (axis_tuples); |
1013 | | if (unlikely (!var.indices.allocate_from_pool (pool, point_count) || |
1014 | | !var.deltas_x.allocate_from_pool (pool, point_count, false))) |
1015 | | return false; |
1016 | | |
1017 | | if (is_gvar && unlikely (!var.deltas_y.allocate_from_pool (pool, point_count, false))) |
1018 | | return false; |
1019 | | |
1020 | | for (unsigned i = 0; i < num_deltas; i++) |
1021 | | { |
1022 | | unsigned idx = apply_to_all ? i : indices[i]; |
1023 | | if (idx >= point_count) continue; |
1024 | | var.indices[idx] = true; |
1025 | | var.deltas_x[idx] = deltas_x[i]; |
1026 | | if (is_gvar) |
1027 | | var.deltas_y[idx] = deltas_y[i]; |
1028 | | } |
1029 | | tuple_vars.push (std::move (var)); |
1030 | | } while (iterator.move_to_next ()); |
1031 | | |
1032 | | is_composite = is_composite_glyph; |
1033 | | return true; |
1034 | | } |
1035 | | |
1036 | | bool create_from_item_var_data (const VarData &var_data, |
1037 | | const hb_vector_t<hb_hashmap_t<hb_tag_t, Triple>>& regions, |
1038 | | const hb_map_t& axes_old_index_tag_map, |
1039 | | unsigned& item_count, |
1040 | | const hb_inc_bimap_t* inner_map = nullptr) |
1041 | 0 | { |
1042 | 0 | /* NULL offset, to keep original varidx valid, just return */ |
1043 | 0 | if (&var_data == &Null (VarData)) |
1044 | 0 | return true; |
1045 | 0 |
|
1046 | 0 | unsigned num_regions = var_data.get_region_index_count (); |
1047 | 0 | if (!tuple_vars.alloc (num_regions)) return false; |
1048 | 0 |
|
1049 | 0 | item_count = inner_map ? inner_map->get_population () : var_data.get_item_count (); |
1050 | 0 | if (!item_count) return true; |
1051 | 0 | unsigned row_size = var_data.get_row_size (); |
1052 | 0 | const HBUINT8 *delta_bytes = var_data.get_delta_bytes (); |
1053 | 0 |
|
1054 | 0 | for (unsigned r = 0; r < num_regions; r++) |
1055 | 0 | { |
1056 | 0 | /* In VarData, deltas are organized in rows, convert them into |
1057 | 0 | * column(region) based tuples, resize deltas_x first */ |
1058 | 0 | tuple_delta_t tuple; |
1059 | 0 | if (!tuple.deltas_x.resize_dirty (item_count) || |
1060 | 0 | !tuple.indices.resize_dirty (item_count)) |
1061 | 0 | return false; |
1062 | 0 |
|
1063 | 0 | for (unsigned i = 0; i < item_count; i++) |
1064 | 0 | { |
1065 | 0 | tuple.indices.arrayZ[i] = true; |
1066 | 0 | tuple.deltas_x.arrayZ[i] = var_data.get_item_delta_fast (inner_map ? inner_map->backward (i) : i, |
1067 | 0 | r, delta_bytes, row_size); |
1068 | 0 | } |
1069 | 0 |
|
1070 | 0 | unsigned region_index = var_data.get_region_index (r); |
1071 | 0 | if (region_index >= regions.length) return false; |
1072 | 0 | tuple.axis_tuples = regions.arrayZ[region_index]; |
1073 | 0 |
|
1074 | 0 | tuple_vars.push (std::move (tuple)); |
1075 | 0 | } |
1076 | 0 | return !tuple_vars.in_error (); |
1077 | 0 | } |
1078 | | |
1079 | | private: |
1080 | | static int _cmp_axis_tag (const void *pa, const void *pb) |
1081 | 0 | { |
1082 | 0 | const hb_tag_t *a = (const hb_tag_t*) pa; |
1083 | 0 | const hb_tag_t *b = (const hb_tag_t*) pb; |
1084 | 0 | return (int)(*a) - (int)(*b); |
1085 | 0 | } |
1086 | | |
1087 | | bool change_tuple_variations_axis_limits (const hb_hashmap_t<hb_tag_t, Triple>& normalized_axes_location, |
1088 | | const hb_hashmap_t<hb_tag_t, TripleDistances>& axes_triple_distances, |
1089 | | hb_alloc_pool_t *pool = nullptr) |
1090 | 0 | { |
1091 | 0 | /* sort axis_tag/axis_limits, make result deterministic */ |
1092 | 0 | hb_vector_t<hb_tag_t> axis_tags; |
1093 | 0 | if (!axis_tags.alloc (normalized_axes_location.get_population ())) |
1094 | 0 | return false; |
1095 | 0 | for (auto t : normalized_axes_location.keys ()) |
1096 | 0 | axis_tags.push (t); |
1097 | 0 |
|
1098 | 0 | // Reused vectors for reduced malloc pressure. |
1099 | 0 | rebase_tent_result_scratch_t scratch; |
1100 | 0 | hb_vector_t<tuple_delta_t> out; |
1101 | 0 |
|
1102 | 0 | axis_tags.qsort (_cmp_axis_tag); |
1103 | 0 | for (auto axis_tag : axis_tags) |
1104 | 0 | { |
1105 | 0 | Triple *axis_limit; |
1106 | 0 | if (!normalized_axes_location.has (axis_tag, &axis_limit)) |
1107 | 0 | return false; |
1108 | 0 | TripleDistances axis_triple_distances{1.0, 1.0}; |
1109 | 0 | if (axes_triple_distances.has (axis_tag)) |
1110 | 0 | axis_triple_distances = axes_triple_distances.get (axis_tag); |
1111 | 0 |
|
1112 | 0 | hb_vector_t<tuple_delta_t> new_vars; |
1113 | 0 | for (tuple_delta_t& var : tuple_vars) |
1114 | 0 | { |
1115 | 0 | // This may move var out. |
1116 | 0 | var.change_tuple_var_axis_limit (axis_tag, *axis_limit, axis_triple_distances, out, scratch, pool); |
1117 | 0 | if (!out) continue; |
1118 | 0 |
|
1119 | 0 | unsigned new_len = new_vars.length + out.length; |
1120 | 0 |
|
1121 | 0 | if (unlikely (!new_vars.alloc (new_len, false))) |
1122 | 0 | return false; |
1123 | 0 |
|
1124 | 0 | for (unsigned i = 0; i < out.length; i++) |
1125 | 0 | new_vars.push (std::move (out[i])); |
1126 | 0 | } |
1127 | 0 | tuple_vars = std::move (new_vars); |
1128 | 0 | } |
1129 | 0 | return true; |
1130 | 0 | } |
1131 | | |
1132 | | /* merge tuple variations with overlapping tents, if iup delta optimization |
1133 | | * is enabled, add default deltas to contour_points */ |
1134 | | bool merge_tuple_variations (contour_point_vector_t* contour_points = nullptr) |
1135 | 0 | { |
1136 | 0 | hb_vector_t<tuple_delta_t> new_vars; |
1137 | 0 | // The pre-allocation is essential for address stability of pointers |
1138 | 0 | // we store in the hashmap. |
1139 | 0 | if (unlikely (!new_vars.alloc (tuple_vars.length))) |
1140 | 0 | return false; |
1141 | 0 | hb_hashmap_t<const hb_hashmap_t<hb_tag_t, Triple>*, unsigned> m; |
1142 | 0 | for (tuple_delta_t& var : tuple_vars) |
1143 | 0 | { |
1144 | 0 | /* if all axes are pinned, drop the tuple variation */ |
1145 | 0 | if (var.axis_tuples.is_empty ()) |
1146 | 0 | { |
1147 | 0 | /* if iup_delta_optimize is enabled, add deltas to contour coords */ |
1148 | 0 | if (contour_points && !contour_points->add_deltas (var.deltas_x, |
1149 | 0 | var.deltas_y, |
1150 | 0 | var.indices)) |
1151 | 0 | return false; |
1152 | 0 | continue; |
1153 | 0 | } |
1154 | 0 |
|
1155 | 0 | unsigned *idx; |
1156 | 0 | if (m.has (&(var.axis_tuples), &idx)) |
1157 | 0 | { |
1158 | 0 | new_vars[*idx] += var; |
1159 | 0 | } |
1160 | 0 | else |
1161 | 0 | { |
1162 | 0 | auto *new_var = new_vars.push (); |
1163 | 0 | if (unlikely (new_vars.in_error ())) |
1164 | 0 | return false; |
1165 | 0 | hb_swap (*new_var, var); |
1166 | 0 | if (unlikely (!m.set (&(new_var->axis_tuples), new_vars.length - 1))) |
1167 | 0 | return false; |
1168 | 0 | } |
1169 | 0 | } |
1170 | 0 | m.fini (); // Just in case, since it points into new_vars data. |
1171 | 0 | // Shouldn't be necessary though, since we only move new_vars, not its |
1172 | 0 | // contents. |
1173 | 0 | tuple_vars = std::move (new_vars); |
1174 | 0 | return true; |
1175 | 0 | } |
1176 | | |
1177 | | /* compile all point set and store byte data in a point_set->hb_bytes_t hashmap, |
1178 | | * also update point_set->count map, which will be used in finding shared |
1179 | | * point set*/ |
1180 | | bool compile_all_point_sets () |
1181 | | { |
1182 | | for (const auto& tuple: tuple_vars) |
1183 | | { |
1184 | | const hb_vector_t<bool>* points_set = &(tuple.indices); |
1185 | | if (point_data_map.has (points_set)) |
1186 | | { |
1187 | | unsigned *count; |
1188 | | if (unlikely (!point_set_count_map.has (points_set, &count) || |
1189 | | !point_set_count_map.set (points_set, (*count) + 1))) |
1190 | | return false; |
1191 | | continue; |
1192 | | } |
1193 | | |
1194 | | hb_vector_t<unsigned char> compiled_point_data; |
1195 | | if (!tuple_delta_t::compile_point_set (*points_set, compiled_point_data)) |
1196 | | return false; |
1197 | | |
1198 | | if (!point_data_map.set (points_set, std::move (compiled_point_data)) || |
1199 | | !point_set_count_map.set (points_set, 1)) |
1200 | | return false; |
1201 | | } |
1202 | | return true; |
1203 | | } |
1204 | | |
1205 | | /* find shared points set which saves most bytes */ |
1206 | | void find_shared_points () |
1207 | | { |
1208 | | unsigned max_saved_bytes = 0; |
1209 | | |
1210 | | for (const auto& _ : point_data_map.iter_ref ()) |
1211 | | { |
1212 | | const hb_vector_t<bool>* points_set = _.first; |
1213 | | unsigned data_length = _.second.length; |
1214 | | if (!data_length) continue; |
1215 | | unsigned *count; |
1216 | | if (unlikely (!point_set_count_map.has (points_set, &count) || |
1217 | | *count <= 1)) |
1218 | | { |
1219 | | shared_points_bytes = nullptr; |
1220 | | return; |
1221 | | } |
1222 | | |
1223 | | unsigned saved_bytes = data_length * ((*count) -1); |
1224 | | if (saved_bytes > max_saved_bytes) |
1225 | | { |
1226 | | max_saved_bytes = saved_bytes; |
1227 | | shared_points_bytes = &(_.second); |
1228 | | } |
1229 | | } |
1230 | | } |
1231 | | |
1232 | | bool calc_inferred_deltas (const contour_point_vector_t& contour_points, |
1233 | | hb_vector_t<unsigned> &scratch) |
1234 | 0 | { |
1235 | 0 | for (tuple_delta_t& var : tuple_vars) |
1236 | 0 | if (!var.calc_inferred_deltas (contour_points, scratch)) |
1237 | 0 | return false; |
1238 | 0 |
|
1239 | 0 | return true; |
1240 | 0 | } |
1241 | | |
1242 | | bool iup_optimize (const contour_point_vector_t& contour_points, |
1243 | | optimize_scratch_t &scratch) |
1244 | 0 | { |
1245 | 0 | for (tuple_delta_t& var : tuple_vars) |
1246 | 0 | { |
1247 | 0 | if (!var.optimize (contour_points, is_composite, scratch)) |
1248 | 0 | return false; |
1249 | 0 | } |
1250 | 0 | return true; |
1251 | 0 | } |
1252 | | |
1253 | | public: |
1254 | | bool instantiate (const hb_hashmap_t<hb_tag_t, Triple>& normalized_axes_location, |
1255 | | const hb_hashmap_t<hb_tag_t, TripleDistances>& axes_triple_distances, |
1256 | | optimize_scratch_t &scratch, |
1257 | | hb_alloc_pool_t *pool = nullptr, |
1258 | | contour_point_vector_t* contour_points = nullptr, |
1259 | | bool optimize = false) |
1260 | 0 | { |
1261 | 0 | if (!tuple_vars) return true; |
1262 | 0 | if (!change_tuple_variations_axis_limits (normalized_axes_location, axes_triple_distances, pool)) |
1263 | 0 | return false; |
1264 | 0 | /* compute inferred deltas only for gvar */ |
1265 | 0 | if (contour_points) |
1266 | 0 | { |
1267 | 0 | hb_vector_t<unsigned> scratch; |
1268 | 0 | if (!calc_inferred_deltas (*contour_points, scratch)) |
1269 | 0 | return false; |
1270 | 0 | } |
1271 | 0 |
|
1272 | 0 | /* if iup delta opt is on, contour_points can't be null */ |
1273 | 0 | if (optimize && !contour_points) |
1274 | 0 | return false; |
1275 | 0 |
|
1276 | 0 | if (!merge_tuple_variations (optimize ? contour_points : nullptr)) |
1277 | 0 | return false; |
1278 | 0 |
|
1279 | 0 | if (optimize && !iup_optimize (*contour_points, scratch)) return false; |
1280 | 0 | return !tuple_vars.in_error (); |
1281 | 0 | } |
1282 | | |
1283 | | bool compile_bytes (const hb_map_t& axes_index_map, |
1284 | | const hb_map_t& axes_old_index_tag_map, |
1285 | | bool use_shared_points, |
1286 | | bool is_gvar = false, |
1287 | | const hb_hashmap_t<const hb_vector_t<F2DOT14>*, unsigned>* shared_tuples_idx_map = nullptr, |
1288 | | hb_alloc_pool_t *pool = nullptr) |
1289 | | { |
1290 | | // return true for empty glyph |
1291 | | if (!tuple_vars) |
1292 | | return true; |
1293 | | |
1294 | | // compile points set and store data in hashmap |
1295 | | if (!compile_all_point_sets ()) |
1296 | | return false; |
1297 | | |
1298 | | /* total compiled byte size as TupleVariationData format, initialized to its |
1299 | | * min_size: 4 */ |
1300 | | compiled_byte_size += 4; |
1301 | | |
1302 | | if (use_shared_points) |
1303 | | { |
1304 | | find_shared_points (); |
1305 | | if (shared_points_bytes) |
1306 | | compiled_byte_size += shared_points_bytes->length; |
1307 | | } |
1308 | | hb_vector_t<int> rounded_deltas_scratch; |
1309 | | // compile delta and tuple var header for each tuple variation |
1310 | | for (auto& tuple: tuple_vars) |
1311 | | { |
1312 | | const hb_vector_t<bool>* points_set = &(tuple.indices); |
1313 | | hb_vector_t<unsigned char> *points_data; |
1314 | | if (unlikely (!point_data_map.has (points_set, &points_data))) |
1315 | | return false; |
1316 | | |
1317 | | /* when iup optimization is enabled, num of referenced points could be 0 |
1318 | | * and thus the compiled points bytes is empty, we should skip compiling |
1319 | | * this tuple */ |
1320 | | if (!points_data->length) |
1321 | | continue; |
1322 | | if (!tuple.compile_deltas (rounded_deltas_scratch, pool)) |
1323 | | return false; |
1324 | | |
1325 | | unsigned points_data_length = (points_data != shared_points_bytes) ? points_data->length : 0; |
1326 | | if (!tuple.compile_tuple_var_header (axes_index_map, points_data_length, axes_old_index_tag_map, |
1327 | | shared_tuples_idx_map, |
1328 | | pool)) |
1329 | | return false; |
1330 | | compiled_byte_size += tuple.compiled_tuple_header.length + points_data_length + tuple.compiled_deltas.length; |
1331 | | } |
1332 | | |
1333 | | if (is_gvar && (compiled_byte_size % 2)) |
1334 | | { |
1335 | | needs_padding = true; |
1336 | | compiled_byte_size += 1; |
1337 | | } |
1338 | | |
1339 | | return true; |
1340 | | } |
1341 | | |
1342 | | bool serialize_var_headers (hb_serialize_context_t *c, unsigned& total_header_len) const |
1343 | | { |
1344 | | TRACE_SERIALIZE (this); |
1345 | | for (const auto& tuple: tuple_vars) |
1346 | | { |
1347 | | tuple.compiled_tuple_header.as_array ().copy (c); |
1348 | | if (c->in_error ()) return_trace (false); |
1349 | | total_header_len += tuple.compiled_tuple_header.length; |
1350 | | } |
1351 | | return_trace (true); |
1352 | | } |
1353 | | |
1354 | | bool serialize_var_data (hb_serialize_context_t *c, bool is_gvar) const |
1355 | | { |
1356 | | TRACE_SERIALIZE (this); |
1357 | | if (is_gvar && shared_points_bytes) |
1358 | | { |
1359 | | hb_ubytes_t s (shared_points_bytes->arrayZ, shared_points_bytes->length); |
1360 | | s.copy (c); |
1361 | | } |
1362 | | |
1363 | | for (const auto& tuple: tuple_vars) |
1364 | | { |
1365 | | const hb_vector_t<bool>* points_set = &(tuple.indices); |
1366 | | hb_vector_t<unsigned char> *point_data; |
1367 | | if (!point_data_map.has (points_set, &point_data)) |
1368 | | return_trace (false); |
1369 | | |
1370 | | if (!is_gvar || point_data != shared_points_bytes) |
1371 | | { |
1372 | | hb_ubytes_t s (point_data->arrayZ, point_data->length); |
1373 | | s.copy (c); |
1374 | | } |
1375 | | |
1376 | | tuple.compiled_deltas.as_array ().copy (c); |
1377 | | if (c->in_error ()) return_trace (false); |
1378 | | } |
1379 | | |
1380 | | /* padding for gvar */ |
1381 | | if (is_gvar && needs_padding) |
1382 | | { |
1383 | | HBUINT8 pad; |
1384 | | pad = 0; |
1385 | | if (!c->embed (pad)) return_trace (false); |
1386 | | } |
1387 | | return_trace (true); |
1388 | | } |
1389 | | }; |
1390 | | |
1391 | | struct tuple_iterator_t |
1392 | | { |
1393 | | unsigned get_axis_count () const { return axis_count; } |
1394 | | |
1395 | | void init (hb_bytes_t var_data_bytes_, unsigned int axis_count_, const void *table_base_) |
1396 | 32.6k | { |
1397 | 32.6k | var_data_bytes = var_data_bytes_; |
1398 | 32.6k | var_data = var_data_bytes_.as<TupleVariationData> (); |
1399 | 32.6k | tuples_left = var_data->tupleVarCount.get_count (); |
1400 | 32.6k | axis_count = axis_count_; |
1401 | 32.6k | current_tuple = &var_data->get_tuple_var_header (); |
1402 | 32.6k | data_offset = 0; |
1403 | 32.6k | table_base = table_base_; |
1404 | 32.6k | } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::tuple_iterator_t::init(hb_array_t<char const>, unsigned int, void const*) Line | Count | Source | 1396 | 15.7k | { | 1397 | 15.7k | var_data_bytes = var_data_bytes_; | 1398 | 15.7k | var_data = var_data_bytes_.as<TupleVariationData> (); | 1399 | 15.7k | tuples_left = var_data->tupleVarCount.get_count (); | 1400 | 15.7k | axis_count = axis_count_; | 1401 | 15.7k | current_tuple = &var_data->get_tuple_var_header (); | 1402 | 15.7k | data_offset = 0; | 1403 | 15.7k | table_base = table_base_; | 1404 | 15.7k | } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::tuple_iterator_t::init(hb_array_t<char const>, unsigned int, void const*) Line | Count | Source | 1396 | 16.9k | { | 1397 | 16.9k | var_data_bytes = var_data_bytes_; | 1398 | 16.9k | var_data = var_data_bytes_.as<TupleVariationData> (); | 1399 | 16.9k | tuples_left = var_data->tupleVarCount.get_count (); | 1400 | 16.9k | axis_count = axis_count_; | 1401 | 16.9k | current_tuple = &var_data->get_tuple_var_header (); | 1402 | 16.9k | data_offset = 0; | 1403 | 16.9k | table_base = table_base_; | 1404 | 16.9k | } |
|
1405 | | |
1406 | | bool get_shared_indices (hb_vector_t<unsigned int> &shared_indices /* OUT */) |
1407 | 32.6k | { |
1408 | 32.6k | if (var_data->has_shared_point_numbers ()) |
1409 | 20.1k | { |
1410 | 20.1k | const HBUINT8 *base = &(table_base+var_data->data); |
1411 | 20.1k | const HBUINT8 *p = base; |
1412 | 20.1k | if (!decompile_points (p, shared_indices, (const HBUINT8 *) (var_data_bytes.arrayZ + var_data_bytes.length))) return false; |
1413 | 12.2k | data_offset = p - base; |
1414 | 12.2k | } |
1415 | 24.7k | return true; |
1416 | 32.6k | } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::tuple_iterator_t::get_shared_indices(hb_vector_t<unsigned int, false>&) Line | Count | Source | 1407 | 15.7k | { | 1408 | 15.7k | if (var_data->has_shared_point_numbers ()) | 1409 | 7.30k | { | 1410 | 7.30k | const HBUINT8 *base = &(table_base+var_data->data); | 1411 | 7.30k | const HBUINT8 *p = base; | 1412 | 7.30k | if (!decompile_points (p, shared_indices, (const HBUINT8 *) (var_data_bytes.arrayZ + var_data_bytes.length))) return false; | 1413 | 6.46k | data_offset = p - base; | 1414 | 6.46k | } | 1415 | 14.8k | return true; | 1416 | 15.7k | } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::tuple_iterator_t::get_shared_indices(hb_vector_t<unsigned int, false>&) Line | Count | Source | 1407 | 16.9k | { | 1408 | 16.9k | if (var_data->has_shared_point_numbers ()) | 1409 | 12.8k | { | 1410 | 12.8k | const HBUINT8 *base = &(table_base+var_data->data); | 1411 | 12.8k | const HBUINT8 *p = base; | 1412 | 12.8k | if (!decompile_points (p, shared_indices, (const HBUINT8 *) (var_data_bytes.arrayZ + var_data_bytes.length))) return false; | 1413 | 5.82k | data_offset = p - base; | 1414 | 5.82k | } | 1415 | 9.90k | return true; | 1416 | 16.9k | } |
|
1417 | | |
1418 | | bool is_valid () |
1419 | 164k | { |
1420 | 164k | if (unlikely (tuples_left <= 0)) |
1421 | 1.31k | return false; |
1422 | | |
1423 | 163k | current_tuple_size = TupleVariationHeader::min_size; |
1424 | 163k | if (unlikely (!var_data_bytes.check_range (current_tuple, current_tuple_size))) |
1425 | 3.44k | return false; |
1426 | | |
1427 | 159k | current_tuple_size = current_tuple->get_size (axis_count); |
1428 | 159k | if (unlikely (!var_data_bytes.check_range (current_tuple, current_tuple_size))) |
1429 | 2.94k | return false; |
1430 | | |
1431 | 156k | return true; |
1432 | 159k | } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::tuple_iterator_t::is_valid() Line | Count | Source | 1419 | 99.6k | { | 1420 | 99.6k | if (unlikely (tuples_left <= 0)) | 1421 | 680 | return false; | 1422 | | | 1423 | 99.0k | current_tuple_size = TupleVariationHeader::min_size; | 1424 | 99.0k | if (unlikely (!var_data_bytes.check_range (current_tuple, current_tuple_size))) | 1425 | 1.70k | return false; | 1426 | | | 1427 | 97.3k | current_tuple_size = current_tuple->get_size (axis_count); | 1428 | 97.3k | if (unlikely (!var_data_bytes.check_range (current_tuple, current_tuple_size))) | 1429 | 1.68k | return false; | 1430 | | | 1431 | 95.6k | return true; | 1432 | 97.3k | } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::tuple_iterator_t::is_valid() Line | Count | Source | 1419 | 64.8k | { | 1420 | 64.8k | if (unlikely (tuples_left <= 0)) | 1421 | 635 | return false; | 1422 | | | 1423 | 64.2k | current_tuple_size = TupleVariationHeader::min_size; | 1424 | 64.2k | if (unlikely (!var_data_bytes.check_range (current_tuple, current_tuple_size))) | 1425 | 1.73k | return false; | 1426 | | | 1427 | 62.5k | current_tuple_size = current_tuple->get_size (axis_count); | 1428 | 62.5k | if (unlikely (!var_data_bytes.check_range (current_tuple, current_tuple_size))) | 1429 | 1.26k | return false; | 1430 | | | 1431 | 61.2k | return true; | 1432 | 62.5k | } |
|
1433 | | |
1434 | | HB_ALWAYS_INLINE |
1435 | | bool move_to_next () |
1436 | 139k | { |
1437 | 139k | data_offset += current_tuple->get_data_size (); |
1438 | 139k | current_tuple = &StructAtOffset<TupleVariationHeader> (current_tuple, current_tuple_size); |
1439 | 139k | tuples_left--; |
1440 | 139k | return is_valid (); |
1441 | 139k | } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::tuple_iterator_t::move_to_next() Line | Count | Source | 1436 | 84.8k | { | 1437 | 84.8k | data_offset += current_tuple->get_data_size (); | 1438 | 84.8k | current_tuple = &StructAtOffset<TupleVariationHeader> (current_tuple, current_tuple_size); | 1439 | 84.8k | tuples_left--; | 1440 | 84.8k | return is_valid (); | 1441 | 84.8k | } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::tuple_iterator_t::move_to_next() Line | Count | Source | 1436 | 54.9k | { | 1437 | 54.9k | data_offset += current_tuple->get_data_size (); | 1438 | 54.9k | current_tuple = &StructAtOffset<TupleVariationHeader> (current_tuple, current_tuple_size); | 1439 | 54.9k | tuples_left--; | 1440 | 54.9k | return is_valid (); | 1441 | 54.9k | } |
|
1442 | | |
1443 | | // TODO: Make it return (sanitized) hb_bytes_t |
1444 | | const HBUINT8 *get_serialized_data () const |
1445 | 23.8k | { return &(table_base+var_data->data) + data_offset; } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::tuple_iterator_t::get_serialized_data() const Line | Count | Source | 1445 | 13.2k | { return &(table_base+var_data->data) + data_offset; } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::tuple_iterator_t::get_serialized_data() const Line | Count | Source | 1445 | 10.5k | { return &(table_base+var_data->data) + data_offset; } |
|
1446 | | |
1447 | | private: |
1448 | | signed tuples_left; |
1449 | | const TupleVariationData *var_data; |
1450 | | unsigned int axis_count; |
1451 | | unsigned int data_offset; |
1452 | | unsigned int current_tuple_size; |
1453 | | const void *table_base; |
1454 | | |
1455 | | public: |
1456 | | hb_bytes_t var_data_bytes; |
1457 | | const TupleVariationHeader *current_tuple; |
1458 | | }; |
1459 | | |
1460 | | static bool get_tuple_iterator (hb_bytes_t var_data_bytes, unsigned axis_count, |
1461 | | const void *table_base, |
1462 | | hb_vector_t<unsigned int> &shared_indices /* OUT */, |
1463 | | tuple_iterator_t *iterator /* OUT */) |
1464 | 32.6k | { |
1465 | 32.6k | iterator->init (var_data_bytes, axis_count, table_base); |
1466 | 32.6k | if (!iterator->get_shared_indices (shared_indices)) |
1467 | 7.90k | return false; |
1468 | 24.7k | return iterator->is_valid (); |
1469 | 32.6k | } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::get_tuple_iterator(hb_array_t<char const>, unsigned int, void const*, hb_vector_t<unsigned int, false>&, OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::tuple_iterator_t*) Line | Count | Source | 1464 | 15.7k | { | 1465 | 15.7k | iterator->init (var_data_bytes, axis_count, table_base); | 1466 | 15.7k | if (!iterator->get_shared_indices (shared_indices)) | 1467 | 844 | return false; | 1468 | 14.8k | return iterator->is_valid (); | 1469 | 15.7k | } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::get_tuple_iterator(hb_array_t<char const>, unsigned int, void const*, hb_vector_t<unsigned int, false>&, OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::tuple_iterator_t*) Line | Count | Source | 1464 | 16.9k | { | 1465 | 16.9k | iterator->init (var_data_bytes, axis_count, table_base); | 1466 | 16.9k | if (!iterator->get_shared_indices (shared_indices)) | 1467 | 7.06k | return false; | 1468 | 9.90k | return iterator->is_valid (); | 1469 | 16.9k | } |
|
1470 | | |
1471 | 32.6k | bool has_shared_point_numbers () const { return tupleVarCount.has_shared_point_numbers (); } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::has_shared_point_numbers() const Line | Count | Source | 1471 | 15.7k | bool has_shared_point_numbers () const { return tupleVarCount.has_shared_point_numbers (); } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::has_shared_point_numbers() const Line | Count | Source | 1471 | 16.9k | bool has_shared_point_numbers () const { return tupleVarCount.has_shared_point_numbers (); } |
|
1472 | | |
1473 | | static bool decompile_points (const HBUINT8 *&p /* IN/OUT */, |
1474 | | hb_vector_t<unsigned int> &points /* OUT */, |
1475 | | const HBUINT8 *end) |
1476 | 24.5k | { |
1477 | 24.5k | enum packed_point_flag_t |
1478 | 24.5k | { |
1479 | 24.5k | POINTS_ARE_WORDS = 0x80, |
1480 | 24.5k | POINT_RUN_COUNT_MASK = 0x7F |
1481 | 24.5k | }; |
1482 | | |
1483 | 24.5k | if (unlikely (p + 1 > end)) return false; |
1484 | | |
1485 | 23.6k | unsigned count = *p++; |
1486 | 23.6k | if (count & POINTS_ARE_WORDS) |
1487 | 3.99k | { |
1488 | 3.99k | if (unlikely (p + 1 > end)) return false; |
1489 | 3.69k | count = ((count & POINT_RUN_COUNT_MASK) << 8) | *p++; |
1490 | 3.69k | } |
1491 | 23.3k | if (unlikely (!points.resize_dirty (count))) return false; |
1492 | | |
1493 | 22.7k | unsigned n = 0; |
1494 | 22.7k | unsigned i = 0; |
1495 | 367k | while (i < count) |
1496 | 351k | { |
1497 | 351k | if (unlikely (p + 1 > end)) return false; |
1498 | 349k | unsigned control = *p++; |
1499 | 349k | unsigned run_count = (control & POINT_RUN_COUNT_MASK) + 1; |
1500 | 349k | unsigned stop = i + run_count; |
1501 | 349k | if (unlikely (stop > count)) return false; |
1502 | 346k | if (control & POINTS_ARE_WORDS) |
1503 | 25.6k | { |
1504 | 25.6k | if (unlikely (p + run_count * HBUINT16::static_size > end)) return false; |
1505 | 1.98M | for (; i < stop; i++) |
1506 | 1.96M | { |
1507 | 1.96M | n += *(const HBUINT16 *)p; |
1508 | 1.96M | points.arrayZ[i] = n; |
1509 | 1.96M | p += HBUINT16::static_size; |
1510 | 1.96M | } |
1511 | 24.8k | } |
1512 | 320k | else |
1513 | 320k | { |
1514 | 320k | if (unlikely (p + run_count > end)) return false; |
1515 | 1.85M | for (; i < stop; i++) |
1516 | 1.54M | { |
1517 | 1.54M | n += *p++; |
1518 | 1.54M | points.arrayZ[i] = n; |
1519 | 1.54M | } |
1520 | 319k | } |
1521 | 346k | } |
1522 | 15.3k | return true; |
1523 | 22.7k | } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::decompile_points(OT::NumType<true, unsigned char, 1u> const*&, hb_vector_t<unsigned int, false>&, OT::NumType<true, unsigned char, 1u> const*) Line | Count | Source | 1476 | 10.7k | { | 1477 | 10.7k | enum packed_point_flag_t | 1478 | 10.7k | { | 1479 | 10.7k | POINTS_ARE_WORDS = 0x80, | 1480 | 10.7k | POINT_RUN_COUNT_MASK = 0x7F | 1481 | 10.7k | }; | 1482 | | | 1483 | 10.7k | if (unlikely (p + 1 > end)) return false; | 1484 | | | 1485 | 10.5k | unsigned count = *p++; | 1486 | 10.5k | if (count & POINTS_ARE_WORDS) | 1487 | 968 | { | 1488 | 968 | if (unlikely (p + 1 > end)) return false; | 1489 | 708 | count = ((count & POINT_RUN_COUNT_MASK) << 8) | *p++; | 1490 | 708 | } | 1491 | 10.2k | if (unlikely (!points.resize_dirty (count))) return false; | 1492 | | | 1493 | 10.0k | unsigned n = 0; | 1494 | 10.0k | unsigned i = 0; | 1495 | 34.2k | while (i < count) | 1496 | 25.3k | { | 1497 | 25.3k | if (unlikely (p + 1 > end)) return false; | 1498 | 25.1k | unsigned control = *p++; | 1499 | 25.1k | unsigned run_count = (control & POINT_RUN_COUNT_MASK) + 1; | 1500 | 25.1k | unsigned stop = i + run_count; | 1501 | 25.1k | if (unlikely (stop > count)) return false; | 1502 | 24.8k | if (control & POINTS_ARE_WORDS) | 1503 | 506 | { | 1504 | 506 | if (unlikely (p + run_count * HBUINT16::static_size > end)) return false; | 1505 | 15.4k | for (; i < stop; i++) | 1506 | 15.2k | { | 1507 | 15.2k | n += *(const HBUINT16 *)p; | 1508 | 15.2k | points.arrayZ[i] = n; | 1509 | 15.2k | p += HBUINT16::static_size; | 1510 | 15.2k | } | 1511 | 263 | } | 1512 | 24.3k | else | 1513 | 24.3k | { | 1514 | 24.3k | if (unlikely (p + run_count > end)) return false; | 1515 | 92.6k | for (; i < stop; i++) | 1516 | 68.7k | { | 1517 | 68.7k | n += *p++; | 1518 | 68.7k | points.arrayZ[i] = n; | 1519 | 68.7k | } | 1520 | 23.9k | } | 1521 | 24.8k | } | 1522 | 8.88k | return true; | 1523 | 10.0k | } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::decompile_points(OT::NumType<true, unsigned char, 1u> const*&, hb_vector_t<unsigned int, false>&, OT::NumType<true, unsigned char, 1u> const*) Line | Count | Source | 1476 | 13.7k | { | 1477 | 13.7k | enum packed_point_flag_t | 1478 | 13.7k | { | 1479 | 13.7k | POINTS_ARE_WORDS = 0x80, | 1480 | 13.7k | POINT_RUN_COUNT_MASK = 0x7F | 1481 | 13.7k | }; | 1482 | | | 1483 | 13.7k | if (unlikely (p + 1 > end)) return false; | 1484 | | | 1485 | 13.0k | unsigned count = *p++; | 1486 | 13.0k | if (count & POINTS_ARE_WORDS) | 1487 | 3.02k | { | 1488 | 3.02k | if (unlikely (p + 1 > end)) return false; | 1489 | 2.98k | count = ((count & POINT_RUN_COUNT_MASK) << 8) | *p++; | 1490 | 2.98k | } | 1491 | 13.0k | if (unlikely (!points.resize_dirty (count))) return false; | 1492 | | | 1493 | 12.7k | unsigned n = 0; | 1494 | 12.7k | unsigned i = 0; | 1495 | 333k | while (i < count) | 1496 | 326k | { | 1497 | 326k | if (unlikely (p + 1 > end)) return false; | 1498 | 324k | unsigned control = *p++; | 1499 | 324k | unsigned run_count = (control & POINT_RUN_COUNT_MASK) + 1; | 1500 | 324k | unsigned stop = i + run_count; | 1501 | 324k | if (unlikely (stop > count)) return false; | 1502 | 321k | if (control & POINTS_ARE_WORDS) | 1503 | 25.1k | { | 1504 | 25.1k | if (unlikely (p + run_count * HBUINT16::static_size > end)) return false; | 1505 | 1.97M | for (; i < stop; i++) | 1506 | 1.94M | { | 1507 | 1.94M | n += *(const HBUINT16 *)p; | 1508 | 1.94M | points.arrayZ[i] = n; | 1509 | 1.94M | p += HBUINT16::static_size; | 1510 | 1.94M | } | 1511 | 24.6k | } | 1512 | 296k | else | 1513 | 296k | { | 1514 | 296k | if (unlikely (p + run_count > end)) return false; | 1515 | 1.76M | for (; i < stop; i++) | 1516 | 1.47M | { | 1517 | 1.47M | n += *p++; | 1518 | 1.47M | points.arrayZ[i] = n; | 1519 | 1.47M | } | 1520 | 295k | } | 1521 | 321k | } | 1522 | 6.43k | return true; | 1523 | 12.7k | } |
|
1524 | | |
1525 | | template <typename T> |
1526 | | static bool decompile_deltas (const HBUINT8 *&p /* IN/OUT */, |
1527 | | hb_vector_t<T> &deltas /* IN/OUT */, |
1528 | | const HBUINT8 *end, |
1529 | | bool consume_all = false, |
1530 | | unsigned start = 0) |
1531 | 21.2k | { |
1532 | 21.2k | return TupleValues::decompile (p, deltas, end, consume_all, start); |
1533 | 21.2k | } bool OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::decompile_deltas<int>(OT::NumType<true, unsigned char, 1u> const*&, hb_vector_t<int, false>&, OT::NumType<true, unsigned char, 1u> const*, bool, unsigned int) Line | Count | Source | 1531 | 9.16k | { | 1532 | 9.16k | return TupleValues::decompile (p, deltas, end, consume_all, start); | 1533 | 9.16k | } |
bool OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::decompile_deltas<int>(OT::NumType<true, unsigned char, 1u> const*&, hb_vector_t<int, false>&, OT::NumType<true, unsigned char, 1u> const*, bool, unsigned int) Line | Count | Source | 1531 | 12.0k | { | 1532 | 12.0k | return TupleValues::decompile (p, deltas, end, consume_all, start); | 1533 | 12.0k | } |
|
1534 | | |
1535 | 49.0k | bool has_data () const { return tupleVarCount; } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::has_data() const Line | Count | Source | 1535 | 20.2k | bool has_data () const { return tupleVarCount; } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::has_data() const Line | Count | Source | 1535 | 28.7k | bool has_data () const { return tupleVarCount; } |
|
1536 | | |
1537 | | bool decompile_tuple_variations (unsigned point_count, |
1538 | | bool is_gvar, |
1539 | | tuple_iterator_t iterator, |
1540 | | const hb_map_t *axes_old_index_tag_map, |
1541 | | const hb_vector_t<unsigned> &shared_indices, |
1542 | | const hb_array_t<const F2DOT14> shared_tuples, |
1543 | | tuple_variations_t& tuple_variations, /* OUT */ |
1544 | | hb_alloc_pool_t *pool = nullptr, |
1545 | | bool is_composite_glyph = false) const |
1546 | | { |
1547 | | return tuple_variations.create_from_tuple_var_data (iterator, tupleVarCount, |
1548 | | point_count, is_gvar, |
1549 | | axes_old_index_tag_map, |
1550 | | shared_indices, |
1551 | | shared_tuples, |
1552 | | pool, |
1553 | | is_composite_glyph); |
1554 | | } |
1555 | | |
1556 | | bool serialize (hb_serialize_context_t *c, |
1557 | | bool is_gvar, |
1558 | | const tuple_variations_t& tuple_variations) const |
1559 | | { |
1560 | | TRACE_SERIALIZE (this); |
1561 | | /* empty tuple variations, just return and skip serialization. */ |
1562 | | if (!tuple_variations) return_trace (true); |
1563 | | |
1564 | | auto *out = c->start_embed (this); |
1565 | | if (unlikely (!c->extend_min (out))) return_trace (false); |
1566 | | |
1567 | | if (!c->check_assign (out->tupleVarCount, tuple_variations.get_var_count (), |
1568 | | HB_SERIALIZE_ERROR_INT_OVERFLOW)) return_trace (false); |
1569 | | |
1570 | | unsigned total_header_len = 0; |
1571 | | |
1572 | | if (!tuple_variations.serialize_var_headers (c, total_header_len)) |
1573 | | return_trace (false); |
1574 | | |
1575 | | unsigned data_offset = min_size + total_header_len; |
1576 | | if (!is_gvar) data_offset += 4; |
1577 | | if (!c->check_assign (out->data, data_offset, HB_SERIALIZE_ERROR_INT_OVERFLOW)) return_trace (false); |
1578 | | |
1579 | | return tuple_variations.serialize_var_data (c, is_gvar); |
1580 | | } |
1581 | | |
1582 | | protected: |
1583 | | struct TupleVarCount : HBUINT16 |
1584 | | { |
1585 | | friend struct tuple_variations_t; |
1586 | 32.6k | bool has_shared_point_numbers () const { return ((*this) & SharedPointNumbers); } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::TupleVarCount::has_shared_point_numbers() const Line | Count | Source | 1586 | 15.7k | bool has_shared_point_numbers () const { return ((*this) & SharedPointNumbers); } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::TupleVarCount::has_shared_point_numbers() const Line | Count | Source | 1586 | 16.9k | bool has_shared_point_numbers () const { return ((*this) & SharedPointNumbers); } |
|
1587 | 32.6k | unsigned int get_count () const { return (*this) & CountMask; } OT::TupleVariationData<OT::NumType<true, unsigned int, 3u> >::TupleVarCount::get_count() const Line | Count | Source | 1587 | 15.7k | unsigned int get_count () const { return (*this) & CountMask; } |
OT::TupleVariationData<OT::NumType<true, unsigned short, 2u> >::TupleVarCount::get_count() const Line | Count | Source | 1587 | 16.9k | unsigned int get_count () const { return (*this) & CountMask; } |
|
1588 | | TupleVarCount& operator = (uint16_t i) { HBUINT16::operator= (i); return *this; } |
1589 | | explicit operator bool () const { return get_count (); } |
1590 | | |
1591 | | protected: |
1592 | | enum Flags |
1593 | | { |
1594 | | SharedPointNumbers= 0x8000u, |
1595 | | CountMask = 0x0FFFu |
1596 | | }; |
1597 | | public: |
1598 | | DEFINE_SIZE_STATIC (2); |
1599 | | }; |
1600 | | |
1601 | | TupleVarCount tupleVarCount; /* A packed field. The high 4 bits are flags, and the |
1602 | | * low 12 bits are the number of tuple variation tables |
1603 | | * for this glyph. The number of tuple variation tables |
1604 | | * can be any number between 1 and 4095. */ |
1605 | | OffsetTo<HBUINT8, OffType> |
1606 | | data; /* Offset from the start of the base table |
1607 | | * to the serialized data. */ |
1608 | | /* TupleVariationHeader tupleVariationHeaders[] *//* Array of tuple variation headers. */ |
1609 | | public: |
1610 | | DEFINE_SIZE_MIN (2 + OffType::static_size); |
1611 | | }; |
1612 | | |
1613 | | // TODO: Move tuple_variations_t to outside of TupleVariationData |
1614 | | using tuple_variations_t = TupleVariationData<HBUINT16>::tuple_variations_t; |
1615 | | struct item_variations_t |
1616 | | { |
1617 | | using region_t = const hb_hashmap_t<hb_tag_t, Triple>*; |
1618 | | private: |
1619 | | /* each subtable is decompiled into a tuple_variations_t, in which all tuples |
1620 | | * have the same num of deltas (rows) */ |
1621 | | hb_vector_t<tuple_variations_t> vars; |
1622 | | |
1623 | | /* num of retained rows for each subtable, there're 2 cases when var_data is empty: |
1624 | | * 1. retained item_count is zero |
1625 | | * 2. regions is empty and item_count is non-zero. |
1626 | | * when converting to tuples, both will be dropped because the tuple is empty, |
1627 | | * however, we need to retain 2. as all-zero rows to keep original varidx |
1628 | | * valid, so we need a way to remember the num of rows for each subtable */ |
1629 | | hb_vector_t<unsigned> var_data_num_rows; |
1630 | | |
1631 | | /* original region list, decompiled from item varstore, used when rebuilding |
1632 | | * region list after instantiation */ |
1633 | | hb_vector_t<hb_hashmap_t<hb_tag_t, Triple>> orig_region_list; |
1634 | | |
1635 | | /* region list: vector of Regions, maintain the original order for the regions |
1636 | | * that existed before instantiate (), append the new regions at the end. |
1637 | | * Regions are stored in each tuple already, save pointers only. |
1638 | | * When converting back to item varstore, unused regions will be pruned */ |
1639 | | hb_vector_t<region_t> region_list; |
1640 | | |
1641 | | /* region -> idx map after instantiation and pruning unused regions */ |
1642 | | hb_hashmap_t<region_t, unsigned> region_map; |
1643 | | |
1644 | | /* all delta rows after instantiation */ |
1645 | | hb_vector_t<hb_vector_t<int>> delta_rows; |
1646 | | /* final optimized vector of encoding objects used to assemble the varstore */ |
1647 | | hb_vector_t<delta_row_encoding_t> encodings; |
1648 | | |
1649 | | /* old varidxes -> new var_idxes map */ |
1650 | | hb_map_t varidx_map; |
1651 | | |
1652 | | /* has long words */ |
1653 | | bool has_long = false; |
1654 | | |
1655 | | public: |
1656 | | bool has_long_word () const |
1657 | 0 | { return has_long; } |
1658 | | |
1659 | | const hb_vector_t<region_t>& get_region_list () const |
1660 | 0 | { return region_list; } |
1661 | | |
1662 | | const hb_vector_t<delta_row_encoding_t>& get_vardata_encodings () const |
1663 | 0 | { return encodings; } |
1664 | | |
1665 | | const hb_map_t& get_varidx_map () const |
1666 | 0 | { return varidx_map; } |
1667 | | |
1668 | | bool instantiate (const ItemVariationStore& varStore, |
1669 | | const hb_subset_plan_t *plan, |
1670 | | bool optimize=true, |
1671 | | bool use_no_variation_idx=true, |
1672 | | const hb_array_t <const hb_inc_bimap_t> inner_maps = hb_array_t<const hb_inc_bimap_t> ()) |
1673 | 0 | { |
1674 | 0 | if (!create_from_item_varstore (varStore, plan->axes_old_index_tag_map, inner_maps)) |
1675 | 0 | return false; |
1676 | 0 | if (!instantiate_tuple_vars (plan->axes_location, plan->axes_triple_distances)) |
1677 | 0 | return false; |
1678 | 0 | return as_item_varstore (optimize, use_no_variation_idx); |
1679 | 0 | } |
1680 | | |
1681 | | /* keep below APIs public only for unit test: test-item-varstore */ |
1682 | | bool create_from_item_varstore (const ItemVariationStore& varStore, |
1683 | | const hb_map_t& axes_old_index_tag_map, |
1684 | | const hb_array_t <const hb_inc_bimap_t> inner_maps = hb_array_t<const hb_inc_bimap_t> ()) |
1685 | 0 | { |
1686 | 0 | const VarRegionList& regionList = varStore.get_region_list (); |
1687 | 0 | if (!regionList.get_var_regions (axes_old_index_tag_map, orig_region_list)) |
1688 | 0 | return false; |
1689 | 0 |
|
1690 | 0 | unsigned num_var_data = varStore.get_sub_table_count (); |
1691 | 0 | if (inner_maps && inner_maps.length != num_var_data) return false; |
1692 | 0 | if (!vars.alloc (num_var_data) || |
1693 | 0 | !var_data_num_rows.alloc (num_var_data)) return false; |
1694 | 0 |
|
1695 | 0 | for (unsigned i = 0; i < num_var_data; i++) |
1696 | 0 | { |
1697 | 0 | if (inner_maps && !inner_maps.arrayZ[i].get_population ()) |
1698 | 0 | continue; |
1699 | 0 | tuple_variations_t var_data_tuples; |
1700 | 0 | unsigned item_count = 0; |
1701 | 0 | if (!var_data_tuples.create_from_item_var_data (varStore.get_sub_table (i), |
1702 | 0 | orig_region_list, |
1703 | 0 | axes_old_index_tag_map, |
1704 | 0 | item_count, |
1705 | 0 | inner_maps ? &(inner_maps.arrayZ[i]) : nullptr)) |
1706 | 0 | return false; |
1707 | 0 |
|
1708 | 0 | var_data_num_rows.push (item_count); |
1709 | 0 | vars.push (std::move (var_data_tuples)); |
1710 | 0 | } |
1711 | 0 | return !vars.in_error () && !var_data_num_rows.in_error () && vars.length == var_data_num_rows.length; |
1712 | 0 | } |
1713 | | |
1714 | | bool instantiate_tuple_vars (const hb_hashmap_t<hb_tag_t, Triple>& normalized_axes_location, |
1715 | | const hb_hashmap_t<hb_tag_t, TripleDistances>& axes_triple_distances) |
1716 | 0 | { |
1717 | 0 | optimize_scratch_t scratch; |
1718 | 0 | for (tuple_variations_t& tuple_vars : vars) |
1719 | 0 | if (!tuple_vars.instantiate (normalized_axes_location, axes_triple_distances, scratch)) |
1720 | 0 | return false; |
1721 | 0 |
|
1722 | 0 | if (!build_region_list ()) return false; |
1723 | 0 | return true; |
1724 | 0 | } |
1725 | | |
1726 | | bool build_region_list () |
1727 | 0 | { |
1728 | 0 | /* scan all tuples and collect all unique regions, prune unused regions */ |
1729 | 0 | hb_hashmap_t<region_t, unsigned> all_regions; |
1730 | 0 | hb_hashmap_t<region_t, unsigned> used_regions; |
1731 | 0 |
|
1732 | 0 | /* use a vector when inserting new regions, make result deterministic */ |
1733 | 0 | hb_vector_t<region_t> all_unique_regions; |
1734 | 0 | for (const tuple_variations_t& sub_table : vars) |
1735 | 0 | { |
1736 | 0 | for (const tuple_delta_t& tuple : sub_table.tuple_vars) |
1737 | 0 | { |
1738 | 0 | region_t r = &(tuple.axis_tuples); |
1739 | 0 | if (!used_regions.has (r)) |
1740 | 0 | { |
1741 | 0 | bool all_zeros = true; |
1742 | 0 | for (float d : tuple.deltas_x) |
1743 | 0 | { |
1744 | 0 | int delta = (int) roundf (d); |
1745 | 0 | if (delta != 0) |
1746 | 0 | { |
1747 | 0 | all_zeros = false; |
1748 | 0 | break; |
1749 | 0 | } |
1750 | 0 | } |
1751 | 0 | if (!all_zeros) |
1752 | 0 | { |
1753 | 0 | if (!used_regions.set (r, 1)) |
1754 | 0 | return false; |
1755 | 0 | } |
1756 | 0 | } |
1757 | 0 | if (all_regions.has (r)) |
1758 | 0 | continue; |
1759 | 0 | if (!all_regions.set (r, 1)) |
1760 | 0 | return false; |
1761 | 0 | all_unique_regions.push (r); |
1762 | 0 | } |
1763 | 0 | } |
1764 | 0 |
|
1765 | 0 | /* regions are empty means no variation data, return true */ |
1766 | 0 | if (!all_regions || !all_unique_regions) return true; |
1767 | 0 |
|
1768 | 0 | if (!region_list.alloc (all_regions.get_population ())) |
1769 | 0 | return false; |
1770 | 0 |
|
1771 | 0 | unsigned idx = 0; |
1772 | 0 | /* append the original regions that pre-existed */ |
1773 | 0 | for (const auto& r : orig_region_list) |
1774 | 0 | { |
1775 | 0 | if (!all_regions.has (&r) || !used_regions.has (&r)) |
1776 | 0 | continue; |
1777 | 0 |
|
1778 | 0 | region_list.push (&r); |
1779 | 0 | if (!region_map.set (&r, idx)) |
1780 | 0 | return false; |
1781 | 0 | all_regions.del (&r); |
1782 | 0 | idx++; |
1783 | 0 | } |
1784 | 0 |
|
1785 | 0 | /* append the new regions at the end */ |
1786 | 0 | for (const auto& r: all_unique_regions) |
1787 | 0 | { |
1788 | 0 | if (!all_regions.has (r) || !used_regions.has (r)) |
1789 | 0 | continue; |
1790 | 0 | region_list.push (r); |
1791 | 0 | if (!region_map.set (r, idx)) |
1792 | 0 | return false; |
1793 | 0 | all_regions.del (r); |
1794 | 0 | idx++; |
1795 | 0 | } |
1796 | 0 | return (!region_list.in_error ()) && (!region_map.in_error ()); |
1797 | 0 | } |
1798 | | |
1799 | | /* main algorithm ported from fonttools VarStore_optimize() method, optimize |
1800 | | * varstore by default */ |
1801 | | |
1802 | | struct combined_gain_idx_tuple_t |
1803 | | { |
1804 | | uint64_t encoded; |
1805 | | |
1806 | | combined_gain_idx_tuple_t () = default; |
1807 | | combined_gain_idx_tuple_t (unsigned gain, unsigned i, unsigned j) |
1808 | | : encoded ((uint64_t (0xFFFFFF - gain) << 40) | (uint64_t (i) << 20) | uint64_t (j)) |
1809 | 0 | { |
1810 | 0 | assert (gain < 0xFFFFFF); |
1811 | 0 | assert (i < 0xFFFFFFF && j < 0xFFFFFFF); |
1812 | 0 | } |
1813 | | |
1814 | | bool operator < (const combined_gain_idx_tuple_t& o) |
1815 | 0 | { |
1816 | 0 | return encoded < o.encoded; |
1817 | 0 | } |
1818 | | |
1819 | | bool operator <= (const combined_gain_idx_tuple_t& o) |
1820 | 0 | { |
1821 | 0 | return encoded <= o.encoded; |
1822 | 0 | } |
1823 | | |
1824 | 0 | unsigned idx_1 () const { return (encoded >> 20) & 0xFFFFF; }; |
1825 | 0 | unsigned idx_2 () const { return encoded & 0xFFFFF; }; |
1826 | | }; |
1827 | | |
1828 | | bool as_item_varstore (bool optimize=true, bool use_no_variation_idx=true) |
1829 | 0 | { |
1830 | 0 | /* return true if no variation data */ |
1831 | 0 | if (!region_list) return true; |
1832 | 0 | unsigned num_cols = region_list.length; |
1833 | 0 | /* pre-alloc a 2D vector for all sub_table's VarData rows */ |
1834 | 0 | unsigned total_rows = 0; |
1835 | 0 | for (unsigned major = 0; major < var_data_num_rows.length; major++) |
1836 | 0 | total_rows += var_data_num_rows[major]; |
1837 | 0 |
|
1838 | 0 | if (!delta_rows.resize (total_rows)) return false; |
1839 | 0 | /* init all rows to [0]*num_cols */ |
1840 | 0 | for (unsigned i = 0; i < total_rows; i++) |
1841 | 0 | if (!(delta_rows[i].resize (num_cols))) return false; |
1842 | 0 |
|
1843 | 0 | /* old VarIdxes -> full encoding_row mapping */ |
1844 | 0 | hb_hashmap_t<unsigned, const hb_vector_t<int>*> front_mapping; |
1845 | 0 | unsigned start_row = 0; |
1846 | 0 | hb_vector_t<delta_row_encoding_t> encoding_objs; |
1847 | 0 |
|
1848 | 0 | /* delta_rows map, used for filtering out duplicate rows */ |
1849 | 0 | hb_vector_t<const hb_vector_t<int> *> major_rows; |
1850 | 0 | hb_hashmap_t<const hb_vector_t<int>*, unsigned> delta_rows_map; |
1851 | 0 | for (unsigned major = 0; major < vars.length; major++) |
1852 | 0 | { |
1853 | 0 | /* deltas are stored in tuples(column based), convert them back into items |
1854 | 0 | * (row based) delta */ |
1855 | 0 | const tuple_variations_t& tuples = vars[major]; |
1856 | 0 | unsigned num_rows = var_data_num_rows[major]; |
1857 | 0 |
|
1858 | 0 | if (!num_rows) continue; |
1859 | 0 |
|
1860 | 0 | for (const tuple_delta_t& tuple: tuples.tuple_vars) |
1861 | 0 | { |
1862 | 0 | if (tuple.deltas_x.length != num_rows) |
1863 | 0 | return false; |
1864 | 0 |
|
1865 | 0 | /* skip unused regions */ |
1866 | 0 | unsigned *col_idx; |
1867 | 0 | if (!region_map.has (&(tuple.axis_tuples), &col_idx)) |
1868 | 0 | continue; |
1869 | 0 |
|
1870 | 0 | for (unsigned i = 0; i < num_rows; i++) |
1871 | 0 | { |
1872 | 0 | int rounded_delta = roundf (tuple.deltas_x[i]); |
1873 | 0 | delta_rows[start_row + i][*col_idx] += rounded_delta; |
1874 | 0 | has_long |= rounded_delta < -65536 || rounded_delta > 65535; |
1875 | 0 | } |
1876 | 0 | } |
1877 | 0 |
|
1878 | 0 | major_rows.reset (); |
1879 | 0 | for (unsigned minor = 0; minor < num_rows; minor++) |
1880 | 0 | { |
1881 | 0 | const hb_vector_t<int>& row = delta_rows[start_row + minor]; |
1882 | 0 | if (use_no_variation_idx) |
1883 | 0 | { |
1884 | 0 | bool all_zeros = true; |
1885 | 0 | for (int delta : row) |
1886 | 0 | { |
1887 | 0 | if (delta != 0) |
1888 | 0 | { |
1889 | 0 | all_zeros = false; |
1890 | 0 | break; |
1891 | 0 | } |
1892 | 0 | } |
1893 | 0 | if (all_zeros) |
1894 | 0 | continue; |
1895 | 0 | } |
1896 | 0 |
|
1897 | 0 | if (!front_mapping.set ((major<<16) + minor, &row)) |
1898 | 0 | return false; |
1899 | 0 |
|
1900 | 0 | if (delta_rows_map.has (&row)) |
1901 | 0 | continue; |
1902 | 0 |
|
1903 | 0 | delta_rows_map.set (&row, 1); |
1904 | 0 |
|
1905 | 0 | major_rows.push (&row); |
1906 | 0 | } |
1907 | 0 |
|
1908 | 0 | if (major_rows) |
1909 | 0 | encoding_objs.push (delta_row_encoding_t (std::move (major_rows), num_cols)); |
1910 | 0 |
|
1911 | 0 | start_row += num_rows; |
1912 | 0 | } |
1913 | 0 |
|
1914 | 0 | /* return directly if no optimization, maintain original VariationIndex so |
1915 | 0 | * varidx_map would be empty */ |
1916 | 0 | if (!optimize) |
1917 | 0 | { |
1918 | 0 | encodings = std::move (encoding_objs); |
1919 | 0 | return !encodings.in_error (); |
1920 | 0 | } |
1921 | 0 |
|
1922 | 0 | /* NOTE: Fonttools instancer always optimizes VarStore from scratch. This |
1923 | 0 | * is too costly for large fonts. So, instead, we retain the encodings of |
1924 | 0 | * the original VarStore, and just try to combine them if possible. This |
1925 | 0 | * is a compromise between optimization and performance and practically |
1926 | 0 | * works very well. */ |
1927 | 0 |
|
1928 | 0 | // This produces slightly smaller results in some cases. |
1929 | 0 | encoding_objs.qsort (); |
1930 | 0 |
|
1931 | 0 | /* main algorithm: repeatedly pick 2 best encodings to combine, and combine them */ |
1932 | 0 | using item_t = hb_priority_queue_t<combined_gain_idx_tuple_t>::item_t; |
1933 | 0 | hb_vector_t<item_t> queue_items; |
1934 | 0 | unsigned num_todos = encoding_objs.length; |
1935 | 0 | for (unsigned i = 0; i < num_todos; i++) |
1936 | 0 | { |
1937 | 0 | for (unsigned j = i + 1; j < num_todos; j++) |
1938 | 0 | { |
1939 | 0 | int combining_gain = encoding_objs.arrayZ[i].gain_from_merging (encoding_objs.arrayZ[j]); |
1940 | 0 | if (combining_gain > 0) |
1941 | 0 | queue_items.push (item_t (combined_gain_idx_tuple_t (combining_gain, i, j), 0)); |
1942 | 0 | } |
1943 | 0 | } |
1944 | 0 |
|
1945 | 0 | hb_priority_queue_t<combined_gain_idx_tuple_t> queue (std::move (queue_items)); |
1946 | 0 |
|
1947 | 0 | hb_bit_set_t removed_todo_idxes; |
1948 | 0 | while (queue) |
1949 | 0 | { |
1950 | 0 | auto t = queue.pop_minimum ().first; |
1951 | 0 | unsigned i = t.idx_1 (); |
1952 | 0 | unsigned j = t.idx_2 (); |
1953 | 0 |
|
1954 | 0 | if (removed_todo_idxes.has (i) || removed_todo_idxes.has (j)) |
1955 | 0 | continue; |
1956 | 0 |
|
1957 | 0 | delta_row_encoding_t& encoding = encoding_objs.arrayZ[i]; |
1958 | 0 | delta_row_encoding_t& other_encoding = encoding_objs.arrayZ[j]; |
1959 | 0 |
|
1960 | 0 | removed_todo_idxes.add (i); |
1961 | 0 | removed_todo_idxes.add (j); |
1962 | 0 |
|
1963 | 0 | encoding.merge (other_encoding); |
1964 | 0 |
|
1965 | 0 | for (unsigned idx = 0; idx < encoding_objs.length; idx++) |
1966 | 0 | { |
1967 | 0 | if (removed_todo_idxes.has (idx)) continue; |
1968 | 0 |
|
1969 | 0 | const delta_row_encoding_t& obj = encoding_objs.arrayZ[idx]; |
1970 | 0 | // In the unlikely event that the same encoding exists already, combine it. |
1971 | 0 | if (obj.width == encoding.width && obj.chars == encoding.chars) |
1972 | 0 | { |
1973 | 0 | // This is straight port from fonttools algorithm. I added this branch there |
1974 | 0 | // because I thought it can happen. But looks like we never get in here in |
1975 | 0 | // practice. I'm not confident enough to remove it though; in theory it can |
1976 | 0 | // happen. I think it's just that our tests are not extensive enough to hit |
1977 | 0 | // this path. |
1978 | 0 |
|
1979 | 0 | for (const auto& row : obj.items) |
1980 | 0 | encoding.add_row (row); |
1981 | 0 |
|
1982 | 0 | removed_todo_idxes.add (idx); |
1983 | 0 | continue; |
1984 | 0 | } |
1985 | 0 |
|
1986 | 0 | int combined_gain = encoding.gain_from_merging (obj); |
1987 | 0 | if (combined_gain > 0) |
1988 | 0 | queue.insert (combined_gain_idx_tuple_t (combined_gain, idx, encoding_objs.length), 0); |
1989 | 0 | } |
1990 | 0 |
|
1991 | 0 | auto moved_encoding = std::move (encoding); |
1992 | 0 | encoding_objs.push (moved_encoding); |
1993 | 0 | } |
1994 | 0 |
|
1995 | 0 | int num_final_encodings = (int) encoding_objs.length - (int) removed_todo_idxes.get_population (); |
1996 | 0 | if (num_final_encodings <= 0) return false; |
1997 | 0 |
|
1998 | 0 | if (!encodings.alloc (num_final_encodings)) return false; |
1999 | 0 | for (unsigned i = 0; i < encoding_objs.length; i++) |
2000 | 0 | { |
2001 | 0 | if (removed_todo_idxes.has (i)) continue; |
2002 | 0 | encodings.push (std::move (encoding_objs.arrayZ[i])); |
2003 | 0 | } |
2004 | 0 |
|
2005 | 0 | return compile_varidx_map (front_mapping); |
2006 | 0 | } |
2007 | | |
2008 | | private: |
2009 | | /* compile varidx_map for one VarData subtable (index specified by major) */ |
2010 | | bool compile_varidx_map (const hb_hashmap_t<unsigned, const hb_vector_t<int>*>& front_mapping) |
2011 | 0 | { |
2012 | 0 | /* full encoding_row -> new VarIdxes mapping */ |
2013 | 0 | hb_hashmap_t<const hb_vector_t<int>*, unsigned> back_mapping; |
2014 | 0 |
|
2015 | 0 | for (unsigned major = 0; major < encodings.length; major++) |
2016 | 0 | { |
2017 | 0 | delta_row_encoding_t& encoding = encodings[major]; |
2018 | 0 | /* just sanity check, this shouldn't happen */ |
2019 | 0 | if (encoding.is_empty ()) |
2020 | 0 | return false; |
2021 | 0 |
|
2022 | 0 | unsigned num_rows = encoding.items.length; |
2023 | 0 |
|
2024 | 0 | /* sort rows, make result deterministic */ |
2025 | 0 | encoding.items.qsort (_cmp_row); |
2026 | 0 |
|
2027 | 0 | /* compile old to new var_idxes mapping */ |
2028 | 0 | for (unsigned minor = 0; minor < num_rows; minor++) |
2029 | 0 | { |
2030 | 0 | unsigned new_varidx = (major << 16) + minor; |
2031 | 0 | back_mapping.set (encoding.items.arrayZ[minor], new_varidx); |
2032 | 0 | } |
2033 | 0 | } |
2034 | 0 |
|
2035 | 0 | for (auto _ : front_mapping.iter ()) |
2036 | 0 | { |
2037 | 0 | unsigned old_varidx = _.first; |
2038 | 0 | unsigned *new_varidx; |
2039 | 0 | if (back_mapping.has (_.second, &new_varidx)) |
2040 | 0 | varidx_map.set (old_varidx, *new_varidx); |
2041 | 0 | else |
2042 | 0 | varidx_map.set (old_varidx, HB_OT_LAYOUT_NO_VARIATIONS_INDEX); |
2043 | 0 | } |
2044 | 0 | return !varidx_map.in_error (); |
2045 | 0 | } |
2046 | | |
2047 | | static int _cmp_row (const void *pa, const void *pb) |
2048 | 0 | { |
2049 | 0 | /* compare pointers of vectors(const hb_vector_t<int>*) that represent a row */ |
2050 | 0 | const hb_vector_t<int>** a = (const hb_vector_t<int>**) pa; |
2051 | 0 | const hb_vector_t<int>** b = (const hb_vector_t<int>**) pb; |
2052 | 0 |
|
2053 | 0 | for (unsigned i = 0; i < (*b)->length; i++) |
2054 | 0 | { |
2055 | 0 | int va = (*a)->arrayZ[i]; |
2056 | 0 | int vb = (*b)->arrayZ[i]; |
2057 | 0 | if (va != vb) |
2058 | 0 | return va < vb ? -1 : 1; |
2059 | 0 | } |
2060 | 0 | return 0; |
2061 | 0 | } |
2062 | | }; |
2063 | | |
2064 | | |
2065 | | } /* namespace OT */ |
2066 | | |
2067 | | |
2068 | | #endif /* HB_OT_VAR_COMMON_HH */ |