| /* |
| * Copyright © 2021 Google, Inc. |
| * |
| * This is part of HarfBuzz, a text shaping library. |
| * |
| * Permission is hereby granted, without written agreement and without |
| * license or royalty fees, to use, copy, modify, and distribute this |
| * software and its documentation for any purpose, provided that the |
| * above copyright notice and the following two paragraphs appear in |
| * all copies of this software. |
| * |
| * IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE TO ANY PARTY FOR |
| * DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES |
| * ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN |
| * IF THE COPYRIGHT HOLDER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH |
| * DAMAGE. |
| * |
| * THE COPYRIGHT HOLDER SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, |
| * BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND |
| * FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS |
| * ON AN "AS IS" BASIS, AND THE COPYRIGHT HOLDER HAS NO OBLIGATION TO |
| * PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. |
| * |
| */ |
| |
| #ifndef HB_OT_VAR_COMMON_HH |
| #define HB_OT_VAR_COMMON_HH |
| |
| #include "hb-ot-layout-common.hh" |
| #include "hb-priority-queue.hh" |
| #include "hb-subset-instancer-iup.hh" |
| |
| |
| namespace OT { |
| |
| |
| /* https://docs.microsoft.com/en-us/typography/opentype/spec/otvarcommonformats#tuplevariationheader */ |
| struct TupleVariationHeader |
| { |
| friend struct tuple_delta_t; |
| unsigned get_size (unsigned axis_count) const |
| { return min_size + get_all_tuples (axis_count).get_size (); } |
| |
| unsigned get_data_size () const { return varDataSize; } |
| |
| const TupleVariationHeader &get_next (unsigned axis_count) const |
| { return StructAtOffset<TupleVariationHeader> (this, get_size (axis_count)); } |
| |
| bool unpack_axis_tuples (unsigned axis_count, |
| const hb_array_t<const F2DOT14> shared_tuples, |
| const hb_map_t *axes_old_index_tag_map, |
| hb_hashmap_t<hb_tag_t, Triple>& axis_tuples /* OUT */) const |
| { |
| const F2DOT14 *peak_tuple = nullptr; |
| if (has_peak ()) |
| peak_tuple = get_peak_tuple (axis_count).arrayZ; |
| else |
| { |
| unsigned int index = get_index (); |
| if (unlikely ((index + 1) * axis_count > shared_tuples.length)) |
| return false; |
| peak_tuple = shared_tuples.sub_array (axis_count * index, axis_count).arrayZ; |
| } |
| |
| const F2DOT14 *start_tuple = nullptr; |
| const F2DOT14 *end_tuple = nullptr; |
| bool has_interm = has_intermediate (); |
| |
| if (has_interm) |
| { |
| start_tuple = get_start_tuple (axis_count).arrayZ; |
| end_tuple = get_end_tuple (axis_count).arrayZ; |
| } |
| |
| for (unsigned i = 0; i < axis_count; i++) |
| { |
| float peak = peak_tuple[i].to_float (); |
| if (peak == 0.f) continue; |
| |
| hb_tag_t *axis_tag; |
| if (!axes_old_index_tag_map->has (i, &axis_tag)) |
| return false; |
| |
| float start, end; |
| if (has_interm) |
| { |
| start = start_tuple[i].to_float (); |
| end = end_tuple[i].to_float (); |
| } |
| else |
| { |
| start = hb_min (peak, 0.f); |
| end = hb_max (peak, 0.f); |
| } |
| axis_tuples.set (*axis_tag, Triple ((double) start, (double) peak, (double) end)); |
| } |
| |
| return true; |
| } |
| |
| double calculate_scalar (hb_array_t<const int> coords, unsigned int coord_count, |
| const hb_array_t<const F2DOT14> shared_tuples, |
| const hb_vector_t<hb_pair_t<int,int>> *shared_tuple_active_idx = nullptr) const |
| { |
| const F2DOT14 *peak_tuple; |
| |
| unsigned start_idx = 0; |
| unsigned end_idx = coord_count; |
| unsigned step = 1; |
| |
| if (has_peak ()) |
| peak_tuple = get_peak_tuple (coord_count).arrayZ; |
| else |
| { |
| unsigned int index = get_index (); |
| if (unlikely ((index + 1) * coord_count > shared_tuples.length)) |
| return 0.0; |
| peak_tuple = shared_tuples.sub_array (coord_count * index, coord_count).arrayZ; |
| |
| if (shared_tuple_active_idx) |
| { |
| if (unlikely (index >= shared_tuple_active_idx->length)) |
| return 0.0; |
| auto _ = (*shared_tuple_active_idx).arrayZ[index]; |
| if (_.second != -1) |
| { |
| start_idx = _.first; |
| end_idx = _.second + 1; |
| step = _.second - _.first; |
| } |
| else if (_.first != -1) |
| { |
| start_idx = _.first; |
| end_idx = start_idx + 1; |
| } |
| } |
| } |
| |
| const F2DOT14 *start_tuple = nullptr; |
| const F2DOT14 *end_tuple = nullptr; |
| bool has_interm = has_intermediate (); |
| if (has_interm) |
| { |
| start_tuple = get_start_tuple (coord_count).arrayZ; |
| end_tuple = get_end_tuple (coord_count).arrayZ; |
| } |
| |
| double scalar = 1.0; |
| for (unsigned int i = start_idx; i < end_idx; i += step) |
| { |
| int peak = peak_tuple[i].to_int (); |
| if (!peak) continue; |
| |
| int v = coords[i]; |
| if (v == peak) continue; |
| |
| if (has_interm) |
| { |
| int start = start_tuple[i].to_int (); |
| int end = end_tuple[i].to_int (); |
| if (unlikely (start > peak || peak > end || |
| (start < 0 && end > 0 && peak))) continue; |
| if (v < start || v > end) return 0.0; |
| if (v < peak) |
| { if (peak != start) scalar *= (double) (v - start) / (peak - start); } |
| else |
| { if (peak != end) scalar *= (double) (end - v) / (end - peak); } |
| } |
| else if (!v || v < hb_min (0, peak) || v > hb_max (0, peak)) return 0.0; |
| else |
| scalar *= (double) v / peak; |
| } |
| return scalar; |
| } |
| |
| bool has_peak () const { return tupleIndex & TuppleIndex::EmbeddedPeakTuple; } |
| bool has_intermediate () const { return tupleIndex & TuppleIndex::IntermediateRegion; } |
| bool has_private_points () const { return tupleIndex & TuppleIndex::PrivatePointNumbers; } |
| unsigned get_index () const { return tupleIndex & TuppleIndex::TupleIndexMask; } |
| |
| protected: |
| struct TuppleIndex : HBUINT16 |
| { |
| enum Flags { |
| EmbeddedPeakTuple = 0x8000u, |
| IntermediateRegion = 0x4000u, |
| PrivatePointNumbers = 0x2000u, |
| TupleIndexMask = 0x0FFFu |
| }; |
| |
| TuppleIndex& operator = (uint16_t i) { HBUINT16::operator= (i); return *this; } |
| DEFINE_SIZE_STATIC (2); |
| }; |
| |
| hb_array_t<const F2DOT14> get_all_tuples (unsigned axis_count) const |
| { return StructAfter<UnsizedArrayOf<F2DOT14>> (tupleIndex).as_array ((has_peak () + has_intermediate () * 2) * axis_count); } |
| hb_array_t<const F2DOT14> get_peak_tuple (unsigned axis_count) const |
| { return get_all_tuples (axis_count).sub_array (0, axis_count); } |
| hb_array_t<const F2DOT14> get_start_tuple (unsigned axis_count) const |
| { return get_all_tuples (axis_count).sub_array (has_peak () * axis_count, axis_count); } |
| hb_array_t<const F2DOT14> get_end_tuple (unsigned axis_count) const |
| { return get_all_tuples (axis_count).sub_array (has_peak () * axis_count + axis_count, axis_count); } |
| |
| HBUINT16 varDataSize; /* The size in bytes of the serialized |
| * data for this tuple variation table. */ |
| TuppleIndex tupleIndex; /* A packed field. The high 4 bits are flags (see below). |
| The low 12 bits are an index into a shared tuple |
| records array. */ |
| /* UnsizedArrayOf<F2DOT14> peakTuple - optional */ |
| /* Peak tuple record for this tuple variation table — optional, |
| * determined by flags in the tupleIndex value. |
| * |
| * Note that this must always be included in the 'cvar' table. */ |
| /* UnsizedArrayOf<F2DOT14> intermediateStartTuple - optional */ |
| /* Intermediate start tuple record for this tuple variation table — optional, |
| determined by flags in the tupleIndex value. */ |
| /* UnsizedArrayOf<F2DOT14> intermediateEndTuple - optional */ |
| /* Intermediate end tuple record for this tuple variation table — optional, |
| * determined by flags in the tupleIndex value. */ |
| public: |
| DEFINE_SIZE_MIN (4); |
| }; |
| |
| struct tuple_delta_t |
| { |
| static constexpr bool realloc_move = true; // Watch out when adding new members! |
| |
| public: |
| hb_hashmap_t<hb_tag_t, Triple> axis_tuples; |
| |
| /* indices_length = point_count, indice[i] = 1 means point i is referenced */ |
| hb_vector_t<bool> indices; |
| |
| hb_vector_t<double> deltas_x; |
| /* empty for cvar tuples */ |
| hb_vector_t<double> deltas_y; |
| |
| /* compiled data: header and deltas |
| * compiled point data is saved in a hashmap within tuple_variations_t cause |
| * some point sets might be reused by different tuple variations */ |
| hb_vector_t<char> compiled_tuple_header; |
| hb_vector_t<char> compiled_deltas; |
| |
| /* compiled peak coords, empty for non-gvar tuples */ |
| hb_vector_t<char> compiled_peak_coords; |
| |
| tuple_delta_t () = default; |
| tuple_delta_t (const tuple_delta_t& o) = default; |
| |
| friend void swap (tuple_delta_t& a, tuple_delta_t& b) noexcept |
| { |
| hb_swap (a.axis_tuples, b.axis_tuples); |
| hb_swap (a.indices, b.indices); |
| hb_swap (a.deltas_x, b.deltas_x); |
| hb_swap (a.deltas_y, b.deltas_y); |
| hb_swap (a.compiled_tuple_header, b.compiled_tuple_header); |
| hb_swap (a.compiled_deltas, b.compiled_deltas); |
| hb_swap (a.compiled_peak_coords, b.compiled_peak_coords); |
| } |
| |
| tuple_delta_t (tuple_delta_t&& o) noexcept : tuple_delta_t () |
| { hb_swap (*this, o); } |
| |
| tuple_delta_t& operator = (tuple_delta_t&& o) noexcept |
| { |
| hb_swap (*this, o); |
| return *this; |
| } |
| |
| void remove_axis (hb_tag_t axis_tag) |
| { axis_tuples.del (axis_tag); } |
| |
| bool set_tent (hb_tag_t axis_tag, Triple tent) |
| { return axis_tuples.set (axis_tag, tent); } |
| |
| tuple_delta_t& operator += (const tuple_delta_t& o) |
| { |
| unsigned num = indices.length; |
| for (unsigned i = 0; i < num; i++) |
| { |
| if (indices.arrayZ[i]) |
| { |
| if (o.indices.arrayZ[i]) |
| { |
| deltas_x[i] += o.deltas_x[i]; |
| if (deltas_y && o.deltas_y) |
| deltas_y[i] += o.deltas_y[i]; |
| } |
| } |
| else |
| { |
| if (!o.indices.arrayZ[i]) continue; |
| indices.arrayZ[i] = true; |
| deltas_x[i] = o.deltas_x[i]; |
| if (deltas_y && o.deltas_y) |
| deltas_y[i] = o.deltas_y[i]; |
| } |
| } |
| return *this; |
| } |
| |
| tuple_delta_t& operator *= (double scalar) |
| { |
| if (scalar == 1.0) |
| return *this; |
| |
| unsigned num = indices.length; |
| if (deltas_y) |
| for (unsigned i = 0; i < num; i++) |
| { |
| if (!indices.arrayZ[i]) continue; |
| deltas_x[i] *= scalar; |
| deltas_y[i] *= scalar; |
| } |
| else |
| for (unsigned i = 0; i < num; i++) |
| { |
| if (!indices.arrayZ[i]) continue; |
| deltas_x[i] *= scalar; |
| } |
| return *this; |
| } |
| |
| hb_vector_t<tuple_delta_t> change_tuple_var_axis_limit (hb_tag_t axis_tag, Triple axis_limit, |
| TripleDistances axis_triple_distances) const |
| { |
| hb_vector_t<tuple_delta_t> out; |
| Triple *tent; |
| if (!axis_tuples.has (axis_tag, &tent)) |
| { |
| out.push (*this); |
| return out; |
| } |
| |
| if ((tent->minimum < 0.0 && tent->maximum > 0.0) || |
| !(tent->minimum <= tent->middle && tent->middle <= tent->maximum)) |
| return out; |
| |
| if (tent->middle == 0.0) |
| { |
| out.push (*this); |
| return out; |
| } |
| |
| rebase_tent_result_t solutions = rebase_tent (*tent, axis_limit, axis_triple_distances); |
| for (auto &t : solutions) |
| { |
| tuple_delta_t new_var = *this; |
| if (t.second == Triple ()) |
| new_var.remove_axis (axis_tag); |
| else |
| new_var.set_tent (axis_tag, t.second); |
| |
| new_var *= t.first; |
| out.push (std::move (new_var)); |
| } |
| |
| return out; |
| } |
| |
| bool compile_peak_coords (const hb_map_t& axes_index_map, |
| const hb_map_t& axes_old_index_tag_map) |
| { |
| unsigned axis_count = axes_index_map.get_population (); |
| if (unlikely (!compiled_peak_coords.alloc (axis_count * F2DOT14::static_size))) |
| return false; |
| |
| unsigned orig_axis_count = axes_old_index_tag_map.get_population (); |
| for (unsigned i = 0; i < orig_axis_count; i++) |
| { |
| if (!axes_index_map.has (i)) |
| continue; |
| |
| hb_tag_t axis_tag = axes_old_index_tag_map.get (i); |
| Triple *coords; |
| F2DOT14 peak_coord; |
| if (axis_tuples.has (axis_tag, &coords)) |
| peak_coord.set_float (coords->middle); |
| else |
| peak_coord.set_int (0); |
| |
| /* push F2DOT14 value into char vector */ |
| int16_t val = peak_coord.to_int (); |
| compiled_peak_coords.push (static_cast<char> (val >> 8)); |
| compiled_peak_coords.push (static_cast<char> (val & 0xFF)); |
| } |
| |
| return !compiled_peak_coords.in_error (); |
| } |
| |
| /* deltas should be compiled already before we compile tuple |
| * variation header cause we need to fill in the size of the |
| * serialized data for this tuple variation */ |
| bool compile_tuple_var_header (const hb_map_t& axes_index_map, |
| unsigned points_data_length, |
| const hb_map_t& axes_old_index_tag_map, |
| const hb_hashmap_t<const hb_vector_t<char>*, unsigned>* shared_tuples_idx_map) |
| { |
| /* compiled_deltas could be empty after iup delta optimization, we can skip |
| * compiling this tuple and return true */ |
| if (!compiled_deltas) return true; |
| |
| unsigned cur_axis_count = axes_index_map.get_population (); |
| /* allocate enough memory: 1 peak + 2 intermediate coords + fixed header size */ |
| unsigned alloc_len = 3 * cur_axis_count * (F2DOT14::static_size) + 4; |
| if (unlikely (!compiled_tuple_header.resize (alloc_len))) return false; |
| |
| unsigned flag = 0; |
| /* skip the first 4 header bytes: variationDataSize+tupleIndex */ |
| F2DOT14* p = reinterpret_cast<F2DOT14 *> (compiled_tuple_header.begin () + 4); |
| F2DOT14* end = reinterpret_cast<F2DOT14 *> (compiled_tuple_header.end ()); |
| hb_array_t<F2DOT14> coords (p, end - p); |
| |
| /* encode peak coords */ |
| unsigned peak_count = 0; |
| unsigned *shared_tuple_idx; |
| if (shared_tuples_idx_map && |
| shared_tuples_idx_map->has (&compiled_peak_coords, &shared_tuple_idx)) |
| { |
| flag = *shared_tuple_idx; |
| } |
| else |
| { |
| peak_count = encode_peak_coords(coords, flag, axes_index_map, axes_old_index_tag_map); |
| if (!peak_count) return false; |
| } |
| |
| /* encode interim coords, it's optional so returned num could be 0 */ |
| unsigned interim_count = encode_interm_coords (coords.sub_array (peak_count), flag, axes_index_map, axes_old_index_tag_map); |
| |
| /* pointdata length = 0 implies "use shared points" */ |
| if (points_data_length) |
| flag |= TupleVariationHeader::TuppleIndex::PrivatePointNumbers; |
| |
| unsigned serialized_data_size = points_data_length + compiled_deltas.length; |
| TupleVariationHeader *o = reinterpret_cast<TupleVariationHeader *> (compiled_tuple_header.begin ()); |
| o->varDataSize = serialized_data_size; |
| o->tupleIndex = flag; |
| |
| unsigned total_header_len = 4 + (peak_count + interim_count) * (F2DOT14::static_size); |
| return compiled_tuple_header.resize (total_header_len); |
| } |
| |
| unsigned encode_peak_coords (hb_array_t<F2DOT14> peak_coords, |
| unsigned& flag, |
| const hb_map_t& axes_index_map, |
| const hb_map_t& axes_old_index_tag_map) const |
| { |
| unsigned orig_axis_count = axes_old_index_tag_map.get_population (); |
| auto it = peak_coords.iter (); |
| unsigned count = 0; |
| for (unsigned i = 0; i < orig_axis_count; i++) |
| { |
| if (!axes_index_map.has (i)) /* axis pinned */ |
| continue; |
| hb_tag_t axis_tag = axes_old_index_tag_map.get (i); |
| Triple *coords; |
| if (!axis_tuples.has (axis_tag, &coords)) |
| (*it).set_int (0); |
| else |
| (*it).set_float (coords->middle); |
| it++; |
| count++; |
| } |
| flag |= TupleVariationHeader::TuppleIndex::EmbeddedPeakTuple; |
| return count; |
| } |
| |
| /* if no need to encode intermediate coords, then just return p */ |
| unsigned encode_interm_coords (hb_array_t<F2DOT14> coords, |
| unsigned& flag, |
| const hb_map_t& axes_index_map, |
| const hb_map_t& axes_old_index_tag_map) const |
| { |
| unsigned orig_axis_count = axes_old_index_tag_map.get_population (); |
| unsigned cur_axis_count = axes_index_map.get_population (); |
| |
| auto start_coords_iter = coords.sub_array (0, cur_axis_count).iter (); |
| auto end_coords_iter = coords.sub_array (cur_axis_count).iter (); |
| bool encode_needed = false; |
| unsigned count = 0; |
| for (unsigned i = 0; i < orig_axis_count; i++) |
| { |
| if (!axes_index_map.has (i)) /* axis pinned */ |
| continue; |
| hb_tag_t axis_tag = axes_old_index_tag_map.get (i); |
| Triple *coords; |
| float min_val = 0.f, val = 0.f, max_val = 0.f; |
| if (axis_tuples.has (axis_tag, &coords)) |
| { |
| min_val = coords->minimum; |
| val = coords->middle; |
| max_val = coords->maximum; |
| } |
| |
| (*start_coords_iter).set_float (min_val); |
| (*end_coords_iter).set_float (max_val); |
| |
| start_coords_iter++; |
| end_coords_iter++; |
| count += 2; |
| if (min_val != hb_min (val, 0.f) || max_val != hb_max (val, 0.f)) |
| encode_needed = true; |
| } |
| |
| if (encode_needed) |
| { |
| flag |= TupleVariationHeader::TuppleIndex::IntermediateRegion; |
| return count; |
| } |
| return 0; |
| } |
| |
| bool compile_deltas () |
| { return compile_deltas (indices, deltas_x, deltas_y, compiled_deltas); } |
| |
| static bool compile_deltas (const hb_vector_t<bool> &point_indices, |
| const hb_vector_t<double> &x_deltas, |
| const hb_vector_t<double> &y_deltas, |
| hb_vector_t<char> &compiled_deltas /* OUT */) |
| { |
| hb_vector_t<int> rounded_deltas; |
| if (unlikely (!rounded_deltas.alloc (point_indices.length))) |
| return false; |
| |
| for (unsigned i = 0; i < point_indices.length; i++) |
| { |
| if (!point_indices[i]) continue; |
| int rounded_delta = (int) roundf (x_deltas.arrayZ[i]); |
| rounded_deltas.push (rounded_delta); |
| } |
| |
| if (!rounded_deltas) return true; |
| /* allocate enough memories 5 * num_deltas */ |
| unsigned alloc_len = 5 * rounded_deltas.length; |
| if (y_deltas) |
| alloc_len *= 2; |
| |
| if (unlikely (!compiled_deltas.resize (alloc_len))) return false; |
| |
| unsigned encoded_len = compile_deltas (compiled_deltas, rounded_deltas); |
| |
| if (y_deltas) |
| { |
| /* reuse the rounded_deltas vector, check that y_deltas have the same num of deltas as x_deltas */ |
| unsigned j = 0; |
| for (unsigned idx = 0; idx < point_indices.length; idx++) |
| { |
| if (!point_indices[idx]) continue; |
| int rounded_delta = (int) roundf (y_deltas.arrayZ[idx]); |
| |
| if (j >= rounded_deltas.length) return false; |
| |
| rounded_deltas[j++] = rounded_delta; |
| } |
| |
| if (j != rounded_deltas.length) return false; |
| encoded_len += compile_deltas (compiled_deltas.as_array ().sub_array (encoded_len), rounded_deltas); |
| } |
| return compiled_deltas.resize (encoded_len); |
| } |
| |
| static unsigned compile_deltas (hb_array_t<char> encoded_bytes, |
| hb_array_t<const int> deltas) |
| { |
| return TupleValues::compile (deltas, encoded_bytes); |
| } |
| |
| bool calc_inferred_deltas (const contour_point_vector_t& orig_points) |
| { |
| unsigned point_count = orig_points.length; |
| if (point_count != indices.length) |
| return false; |
| |
| unsigned ref_count = 0; |
| hb_vector_t<unsigned> end_points; |
| |
| for (unsigned i = 0; i < point_count; i++) |
| { |
| if (indices.arrayZ[i]) |
| ref_count++; |
| if (orig_points.arrayZ[i].is_end_point) |
| end_points.push (i); |
| } |
| /* all points are referenced, nothing to do */ |
| if (ref_count == point_count) |
| return true; |
| if (unlikely (end_points.in_error ())) return false; |
| |
| hb_set_t inferred_idxes; |
| unsigned start_point = 0; |
| for (unsigned end_point : end_points) |
| { |
| /* Check the number of unreferenced points in a contour. If no unref points or no ref points, nothing to do. */ |
| unsigned unref_count = 0; |
| for (unsigned i = start_point; i < end_point + 1; i++) |
| unref_count += indices.arrayZ[i]; |
| unref_count = (end_point - start_point + 1) - unref_count; |
| |
| unsigned j = start_point; |
| if (unref_count == 0 || unref_count > end_point - start_point) |
| goto no_more_gaps; |
| for (;;) |
| { |
| /* Locate the next gap of unreferenced points between two referenced points prev and next. |
| * Note that a gap may wrap around at left (start_point) and/or at right (end_point). |
| */ |
| unsigned int prev, next, i; |
| for (;;) |
| { |
| i = j; |
| j = next_index (i, start_point, end_point); |
| if (indices.arrayZ[i] && !indices.arrayZ[j]) break; |
| } |
| prev = j = i; |
| for (;;) |
| { |
| i = j; |
| j = next_index (i, start_point, end_point); |
| if (!indices.arrayZ[i] && indices.arrayZ[j]) break; |
| } |
| next = j; |
| /* Infer deltas for all unref points in the gap between prev and next */ |
| i = prev; |
| for (;;) |
| { |
| i = next_index (i, start_point, end_point); |
| if (i == next) break; |
| deltas_x.arrayZ[i] = infer_delta ((double) orig_points.arrayZ[i].x, |
| (double) orig_points.arrayZ[prev].x, |
| (double) orig_points.arrayZ[next].x, |
| deltas_x.arrayZ[prev], deltas_x.arrayZ[next]); |
| deltas_y.arrayZ[i] = infer_delta ((double) orig_points.arrayZ[i].y, |
| (double) orig_points.arrayZ[prev].y, |
| (double) orig_points.arrayZ[next].y, |
| deltas_y.arrayZ[prev], deltas_y.arrayZ[next]); |
| inferred_idxes.add (i); |
| if (--unref_count == 0) goto no_more_gaps; |
| } |
| } |
| no_more_gaps: |
| start_point = end_point + 1; |
| } |
| |
| for (unsigned i = 0; i < point_count; i++) |
| { |
| /* if points are not referenced and deltas are not inferred, set to 0. |
| * reference all points for gvar */ |
| if ( !indices[i]) |
| { |
| if (!inferred_idxes.has (i)) |
| { |
| deltas_x.arrayZ[i] = 0.0; |
| deltas_y.arrayZ[i] = 0.0; |
| } |
| indices[i] = true; |
| } |
| } |
| return true; |
| } |
| |
| bool optimize (const contour_point_vector_t& contour_points, |
| bool is_composite, |
| double tolerance = 0.5 + 1e-10) |
| { |
| unsigned count = contour_points.length; |
| if (deltas_x.length != count || |
| deltas_y.length != count) |
| return false; |
| |
| hb_vector_t<bool> opt_indices; |
| hb_vector_t<int> rounded_x_deltas, rounded_y_deltas; |
| |
| if (unlikely (!rounded_x_deltas.alloc (count) || |
| !rounded_y_deltas.alloc (count))) |
| return false; |
| |
| for (unsigned i = 0; i < count; i++) |
| { |
| int rounded_x_delta = (int) roundf (deltas_x.arrayZ[i]); |
| int rounded_y_delta = (int) roundf (deltas_y.arrayZ[i]); |
| rounded_x_deltas.push (rounded_x_delta); |
| rounded_y_deltas.push (rounded_y_delta); |
| } |
| |
| if (!iup_delta_optimize (contour_points, rounded_x_deltas, rounded_y_deltas, opt_indices, tolerance)) |
| return false; |
| |
| unsigned ref_count = 0; |
| for (bool ref_flag : opt_indices) |
| ref_count += ref_flag; |
| |
| if (ref_count == count) return true; |
| |
| hb_vector_t<double> opt_deltas_x, opt_deltas_y; |
| bool is_comp_glyph_wo_deltas = (is_composite && ref_count == 0); |
| if (is_comp_glyph_wo_deltas) |
| { |
| if (unlikely (!opt_deltas_x.resize (count) || |
| !opt_deltas_y.resize (count))) |
| return false; |
| |
| opt_indices.arrayZ[0] = true; |
| for (unsigned i = 1; i < count; i++) |
| opt_indices.arrayZ[i] = false; |
| } |
| |
| hb_vector_t<char> opt_point_data; |
| if (!compile_point_set (opt_indices, opt_point_data)) |
| return false; |
| hb_vector_t<char> opt_deltas_data; |
| if (!compile_deltas (opt_indices, |
| is_comp_glyph_wo_deltas ? opt_deltas_x : deltas_x, |
| is_comp_glyph_wo_deltas ? opt_deltas_y : deltas_y, |
| opt_deltas_data)) |
| return false; |
| |
| hb_vector_t<char> point_data; |
| if (!compile_point_set (indices, point_data)) |
| return false; |
| hb_vector_t<char> deltas_data; |
| if (!compile_deltas (indices, deltas_x, deltas_y, deltas_data)) |
| return false; |
| |
| if (opt_point_data.length + opt_deltas_data.length < point_data.length + deltas_data.length) |
| { |
| indices.fini (); |
| indices = std::move (opt_indices); |
| |
| if (is_comp_glyph_wo_deltas) |
| { |
| deltas_x.fini (); |
| deltas_x = std::move (opt_deltas_x); |
| |
| deltas_y.fini (); |
| deltas_y = std::move (opt_deltas_y); |
| } |
| } |
| return !indices.in_error () && !deltas_x.in_error () && !deltas_y.in_error (); |
| } |
| |
| static bool compile_point_set (const hb_vector_t<bool> &point_indices, |
| hb_vector_t<char>& compiled_points /* OUT */) |
| { |
| unsigned num_points = 0; |
| for (bool i : point_indices) |
| if (i) num_points++; |
| |
| /* when iup optimization is enabled, num of referenced points could be 0 */ |
| if (!num_points) return true; |
| |
| unsigned indices_length = point_indices.length; |
| /* If the points set consists of all points in the glyph, it's encoded with a |
| * single zero byte */ |
| if (num_points == indices_length) |
| return compiled_points.resize (1); |
| |
| /* allocate enough memories: 2 bytes for count + 3 bytes for each point */ |
| unsigned num_bytes = 2 + 3 *num_points; |
| if (unlikely (!compiled_points.resize (num_bytes, false))) |
| return false; |
| |
| unsigned pos = 0; |
| /* binary data starts with the total number of reference points */ |
| if (num_points < 0x80) |
| compiled_points.arrayZ[pos++] = num_points; |
| else |
| { |
| compiled_points.arrayZ[pos++] = ((num_points >> 8) | 0x80); |
| compiled_points.arrayZ[pos++] = num_points & 0xFF; |
| } |
| |
| const unsigned max_run_length = 0x7F; |
| unsigned i = 0; |
| unsigned last_value = 0; |
| unsigned num_encoded = 0; |
| while (i < indices_length && num_encoded < num_points) |
| { |
| unsigned run_length = 0; |
| unsigned header_pos = pos; |
| compiled_points.arrayZ[pos++] = 0; |
| |
| bool use_byte_encoding = false; |
| bool new_run = true; |
| while (i < indices_length && num_encoded < num_points && |
| run_length <= max_run_length) |
| { |
| // find out next referenced point index |
| while (i < indices_length && !point_indices[i]) |
| i++; |
| |
| if (i >= indices_length) break; |
| |
| unsigned cur_value = i; |
| unsigned delta = cur_value - last_value; |
| |
| if (new_run) |
| { |
| use_byte_encoding = (delta <= 0xFF); |
| new_run = false; |
| } |
| |
| if (use_byte_encoding && delta > 0xFF) |
| break; |
| |
| if (use_byte_encoding) |
| compiled_points.arrayZ[pos++] = delta; |
| else |
| { |
| compiled_points.arrayZ[pos++] = delta >> 8; |
| compiled_points.arrayZ[pos++] = delta & 0xFF; |
| } |
| i++; |
| last_value = cur_value; |
| run_length++; |
| num_encoded++; |
| } |
| |
| if (use_byte_encoding) |
| compiled_points.arrayZ[header_pos] = run_length - 1; |
| else |
| compiled_points.arrayZ[header_pos] = (run_length - 1) | 0x80; |
| } |
| return compiled_points.resize (pos, false); |
| } |
| |
| static double infer_delta (double target_val, double prev_val, double next_val, double prev_delta, double next_delta) |
| { |
| if (prev_val == next_val) |
| return (prev_delta == next_delta) ? prev_delta : 0.0; |
| else if (target_val <= hb_min (prev_val, next_val)) |
| return (prev_val < next_val) ? prev_delta : next_delta; |
| else if (target_val >= hb_max (prev_val, next_val)) |
| return (prev_val > next_val) ? prev_delta : next_delta; |
| |
| double r = (target_val - prev_val) / (next_val - prev_val); |
| return prev_delta + r * (next_delta - prev_delta); |
| } |
| |
| static unsigned int next_index (unsigned int i, unsigned int start, unsigned int end) |
| { return (i >= end) ? start : (i + 1); } |
| }; |
| |
| struct TupleVariationData |
| { |
| bool sanitize (hb_sanitize_context_t *c) const |
| { |
| TRACE_SANITIZE (this); |
| // here check on min_size only, TupleVariationHeader and var data will be |
| // checked while accessing through iterator. |
| return_trace (c->check_struct (this)); |
| } |
| |
| unsigned get_size (unsigned axis_count) const |
| { |
| unsigned total_size = min_size; |
| unsigned count = tupleVarCount.get_count (); |
| const TupleVariationHeader *tuple_var_header = &(get_tuple_var_header()); |
| for (unsigned i = 0; i < count; i++) |
| { |
| total_size += tuple_var_header->get_size (axis_count) + tuple_var_header->get_data_size (); |
| tuple_var_header = &tuple_var_header->get_next (axis_count); |
| } |
| |
| return total_size; |
| } |
| |
| const TupleVariationHeader &get_tuple_var_header (void) const |
| { return StructAfter<TupleVariationHeader> (data); } |
| |
| struct tuple_iterator_t; |
| struct tuple_variations_t |
| { |
| hb_vector_t<tuple_delta_t> tuple_vars; |
| |
| private: |
| /* referenced point set->compiled point data map */ |
| hb_hashmap_t<const hb_vector_t<bool>*, hb_vector_t<char>> point_data_map; |
| /* referenced point set-> count map, used in finding shared points */ |
| hb_hashmap_t<const hb_vector_t<bool>*, unsigned> point_set_count_map; |
| |
| /* empty for non-gvar tuples. |
| * shared_points_bytes is a pointer to some value in the point_data_map, |
| * which will be freed during map destruction. Save it for serialization, so |
| * no need to do find_shared_points () again */ |
| hb_vector_t<char> *shared_points_bytes = nullptr; |
| |
| /* total compiled byte size as TupleVariationData format, initialized to its |
| * min_size: 4 */ |
| unsigned compiled_byte_size = 4; |
| |
| /* for gvar iup delta optimization: whether this is a composite glyph */ |
| bool is_composite = false; |
| |
| public: |
| tuple_variations_t () = default; |
| tuple_variations_t (const tuple_variations_t&) = delete; |
| tuple_variations_t& operator=(const tuple_variations_t&) = delete; |
| tuple_variations_t (tuple_variations_t&&) = default; |
| tuple_variations_t& operator=(tuple_variations_t&&) = default; |
| ~tuple_variations_t () = default; |
| |
| explicit operator bool () const { return bool (tuple_vars); } |
| unsigned get_var_count () const |
| { |
| unsigned count = 0; |
| /* when iup delta opt is enabled, compiled_deltas could be empty and we |
| * should skip this tuple */ |
| for (auto& tuple: tuple_vars) |
| if (tuple.compiled_deltas) count++; |
| |
| if (shared_points_bytes && shared_points_bytes->length) |
| count |= TupleVarCount::SharedPointNumbers; |
| return count; |
| } |
| |
| unsigned get_compiled_byte_size () const |
| { return compiled_byte_size; } |
| |
| bool create_from_tuple_var_data (tuple_iterator_t iterator, |
| unsigned tuple_var_count, |
| unsigned point_count, |
| bool is_gvar, |
| const hb_map_t *axes_old_index_tag_map, |
| const hb_vector_t<unsigned> &shared_indices, |
| const hb_array_t<const F2DOT14> shared_tuples, |
| bool is_composite_glyph) |
| { |
| do |
| { |
| const HBUINT8 *p = iterator.get_serialized_data (); |
| unsigned int length = iterator.current_tuple->get_data_size (); |
| if (unlikely (!iterator.var_data_bytes.check_range (p, length))) |
| return false; |
| |
| hb_hashmap_t<hb_tag_t, Triple> axis_tuples; |
| if (!iterator.current_tuple->unpack_axis_tuples (iterator.get_axis_count (), shared_tuples, axes_old_index_tag_map, axis_tuples) |
| || axis_tuples.is_empty ()) |
| return false; |
| |
| hb_vector_t<unsigned> private_indices; |
| bool has_private_points = iterator.current_tuple->has_private_points (); |
| const HBUINT8 *end = p + length; |
| if (has_private_points && |
| !TupleVariationData::decompile_points (p, private_indices, end)) |
| return false; |
| |
| const hb_vector_t<unsigned> &indices = has_private_points ? private_indices : shared_indices; |
| bool apply_to_all = (indices.length == 0); |
| unsigned num_deltas = apply_to_all ? point_count : indices.length; |
| |
| hb_vector_t<int> deltas_x; |
| |
| if (unlikely (!deltas_x.resize (num_deltas, false) || |
| !TupleVariationData::decompile_deltas (p, deltas_x, end))) |
| return false; |
| |
| hb_vector_t<int> deltas_y; |
| if (is_gvar) |
| { |
| if (unlikely (!deltas_y.resize (num_deltas, false) || |
| !TupleVariationData::decompile_deltas (p, deltas_y, end))) |
| return false; |
| } |
| |
| tuple_delta_t var; |
| var.axis_tuples = std::move (axis_tuples); |
| if (unlikely (!var.indices.resize (point_count) || |
| !var.deltas_x.resize (point_count, false))) |
| return false; |
| |
| if (is_gvar && unlikely (!var.deltas_y.resize (point_count, false))) |
| return false; |
| |
| for (unsigned i = 0; i < num_deltas; i++) |
| { |
| unsigned idx = apply_to_all ? i : indices[i]; |
| if (idx >= point_count) continue; |
| var.indices[idx] = true; |
| var.deltas_x[idx] = deltas_x[i]; |
| if (is_gvar) |
| var.deltas_y[idx] = deltas_y[i]; |
| } |
| tuple_vars.push (std::move (var)); |
| } while (iterator.move_to_next ()); |
| |
| is_composite = is_composite_glyph; |
| return true; |
| } |
| |
| bool create_from_item_var_data (const VarData &var_data, |
| const hb_vector_t<hb_hashmap_t<hb_tag_t, Triple>>& regions, |
| const hb_map_t& axes_old_index_tag_map, |
| unsigned& item_count, |
| const hb_inc_bimap_t* inner_map = nullptr) |
| { |
| /* NULL offset, to keep original varidx valid, just return */ |
| if (&var_data == &Null (VarData)) |
| return true; |
| |
| unsigned num_regions = var_data.get_region_index_count (); |
| if (!tuple_vars.alloc (num_regions)) return false; |
| |
| item_count = inner_map ? inner_map->get_population () : var_data.get_item_count (); |
| if (!item_count) return true; |
| unsigned row_size = var_data.get_row_size (); |
| const HBUINT8 *delta_bytes = var_data.get_delta_bytes (); |
| |
| for (unsigned r = 0; r < num_regions; r++) |
| { |
| /* In VarData, deltas are organized in rows, convert them into |
| * column(region) based tuples, resize deltas_x first */ |
| tuple_delta_t tuple; |
| if (!tuple.deltas_x.resize (item_count, false) || |
| !tuple.indices.resize (item_count, false)) |
| return false; |
| |
| for (unsigned i = 0; i < item_count; i++) |
| { |
| tuple.indices.arrayZ[i] = true; |
| tuple.deltas_x.arrayZ[i] = var_data.get_item_delta_fast (inner_map ? inner_map->backward (i) : i, |
| r, delta_bytes, row_size); |
| } |
| |
| unsigned region_index = var_data.get_region_index (r); |
| if (region_index >= regions.length) return false; |
| tuple.axis_tuples = regions.arrayZ[region_index]; |
| |
| tuple_vars.push (std::move (tuple)); |
| } |
| return !tuple_vars.in_error (); |
| } |
| |
| private: |
| static int _cmp_axis_tag (const void *pa, const void *pb) |
| { |
| const hb_tag_t *a = (const hb_tag_t*) pa; |
| const hb_tag_t *b = (const hb_tag_t*) pb; |
| return (int)(*a) - (int)(*b); |
| } |
| |
| bool change_tuple_variations_axis_limits (const hb_hashmap_t<hb_tag_t, Triple>& normalized_axes_location, |
| const hb_hashmap_t<hb_tag_t, TripleDistances>& axes_triple_distances) |
| { |
| /* sort axis_tag/axis_limits, make result deterministic */ |
| hb_vector_t<hb_tag_t> axis_tags; |
| if (!axis_tags.alloc (normalized_axes_location.get_population ())) |
| return false; |
| for (auto t : normalized_axes_location.keys ()) |
| axis_tags.push (t); |
| |
| axis_tags.qsort (_cmp_axis_tag); |
| for (auto axis_tag : axis_tags) |
| { |
| Triple *axis_limit; |
| if (!normalized_axes_location.has (axis_tag, &axis_limit)) |
| return false; |
| TripleDistances axis_triple_distances{1.0, 1.0}; |
| if (axes_triple_distances.has (axis_tag)) |
| axis_triple_distances = axes_triple_distances.get (axis_tag); |
| |
| hb_vector_t<tuple_delta_t> new_vars; |
| for (const tuple_delta_t& var : tuple_vars) |
| { |
| hb_vector_t<tuple_delta_t> out = var.change_tuple_var_axis_limit (axis_tag, *axis_limit, axis_triple_distances); |
| if (!out) continue; |
| |
| unsigned new_len = new_vars.length + out.length; |
| |
| if (unlikely (!new_vars.alloc (new_len, false))) |
| return false; |
| |
| for (unsigned i = 0; i < out.length; i++) |
| new_vars.push (std::move (out[i])); |
| } |
| tuple_vars.fini (); |
| tuple_vars = std::move (new_vars); |
| } |
| return true; |
| } |
| |
| /* merge tuple variations with overlapping tents, if iup delta optimization |
| * is enabled, add default deltas to contour_points */ |
| bool merge_tuple_variations (contour_point_vector_t* contour_points = nullptr) |
| { |
| hb_vector_t<tuple_delta_t> new_vars; |
| hb_hashmap_t<const hb_hashmap_t<hb_tag_t, Triple>*, unsigned> m; |
| unsigned i = 0; |
| for (const tuple_delta_t& var : tuple_vars) |
| { |
| /* if all axes are pinned, drop the tuple variation */ |
| if (var.axis_tuples.is_empty ()) |
| { |
| /* if iup_delta_optimize is enabled, add deltas to contour coords */ |
| if (contour_points && !contour_points->add_deltas (var.deltas_x, |
| var.deltas_y, |
| var.indices)) |
| return false; |
| continue; |
| } |
| |
| unsigned *idx; |
| if (m.has (&(var.axis_tuples), &idx)) |
| { |
| new_vars[*idx] += var; |
| } |
| else |
| { |
| new_vars.push (var); |
| if (!m.set (&(var.axis_tuples), i)) |
| return false; |
| i++; |
| } |
| } |
| tuple_vars.fini (); |
| tuple_vars = std::move (new_vars); |
| return true; |
| } |
| |
| /* compile all point set and store byte data in a point_set->hb_bytes_t hashmap, |
| * also update point_set->count map, which will be used in finding shared |
| * point set*/ |
| bool compile_all_point_sets () |
| { |
| for (const auto& tuple: tuple_vars) |
| { |
| const hb_vector_t<bool>* points_set = &(tuple.indices); |
| if (point_data_map.has (points_set)) |
| { |
| unsigned *count; |
| if (unlikely (!point_set_count_map.has (points_set, &count) || |
| !point_set_count_map.set (points_set, (*count) + 1))) |
| return false; |
| continue; |
| } |
| |
| hb_vector_t<char> compiled_point_data; |
| if (!tuple_delta_t::compile_point_set (*points_set, compiled_point_data)) |
| return false; |
| |
| if (!point_data_map.set (points_set, std::move (compiled_point_data)) || |
| !point_set_count_map.set (points_set, 1)) |
| return false; |
| } |
| return true; |
| } |
| |
| /* find shared points set which saves most bytes */ |
| void find_shared_points () |
| { |
| unsigned max_saved_bytes = 0; |
| |
| for (const auto& _ : point_data_map.iter_ref ()) |
| { |
| const hb_vector_t<bool>* points_set = _.first; |
| unsigned data_length = _.second.length; |
| if (!data_length) continue; |
| unsigned *count; |
| if (unlikely (!point_set_count_map.has (points_set, &count) || |
| *count <= 1)) |
| { |
| shared_points_bytes = nullptr; |
| return; |
| } |
| |
| unsigned saved_bytes = data_length * ((*count) -1); |
| if (saved_bytes > max_saved_bytes) |
| { |
| max_saved_bytes = saved_bytes; |
| shared_points_bytes = &(_.second); |
| } |
| } |
| } |
| |
| bool calc_inferred_deltas (const contour_point_vector_t& contour_points) |
| { |
| for (tuple_delta_t& var : tuple_vars) |
| if (!var.calc_inferred_deltas (contour_points)) |
| return false; |
| |
| return true; |
| } |
| |
| bool iup_optimize (const contour_point_vector_t& contour_points) |
| { |
| for (tuple_delta_t& var : tuple_vars) |
| { |
| if (!var.optimize (contour_points, is_composite)) |
| return false; |
| } |
| return true; |
| } |
| |
| public: |
| bool instantiate (const hb_hashmap_t<hb_tag_t, Triple>& normalized_axes_location, |
| const hb_hashmap_t<hb_tag_t, TripleDistances>& axes_triple_distances, |
| contour_point_vector_t* contour_points = nullptr, |
| bool optimize = false) |
| { |
| if (!tuple_vars) return true; |
| if (!change_tuple_variations_axis_limits (normalized_axes_location, axes_triple_distances)) |
| return false; |
| /* compute inferred deltas only for gvar */ |
| if (contour_points) |
| if (!calc_inferred_deltas (*contour_points)) |
| return false; |
| |
| /* if iup delta opt is on, contour_points can't be null */ |
| if (optimize && !contour_points) |
| return false; |
| |
| if (!merge_tuple_variations (optimize ? contour_points : nullptr)) |
| return false; |
| |
| if (optimize && !iup_optimize (*contour_points)) return false; |
| return !tuple_vars.in_error (); |
| } |
| |
| bool compile_bytes (const hb_map_t& axes_index_map, |
| const hb_map_t& axes_old_index_tag_map, |
| bool use_shared_points, |
| const hb_hashmap_t<const hb_vector_t<char>*, unsigned>* shared_tuples_idx_map = nullptr) |
| { |
| // compile points set and store data in hashmap |
| if (!compile_all_point_sets ()) |
| return false; |
| |
| if (use_shared_points) |
| { |
| find_shared_points (); |
| if (shared_points_bytes) |
| compiled_byte_size += shared_points_bytes->length; |
| } |
| // compile delta and tuple var header for each tuple variation |
| for (auto& tuple: tuple_vars) |
| { |
| const hb_vector_t<bool>* points_set = &(tuple.indices); |
| hb_vector_t<char> *points_data; |
| if (unlikely (!point_data_map.has (points_set, &points_data))) |
| return false; |
| |
| /* when iup optimization is enabled, num of referenced points could be 0 |
| * and thus the compiled points bytes is empty, we should skip compiling |
| * this tuple */ |
| if (!points_data->length) |
| continue; |
| if (!tuple.compile_deltas ()) |
| return false; |
| |
| unsigned points_data_length = (points_data != shared_points_bytes) ? points_data->length : 0; |
| if (!tuple.compile_tuple_var_header (axes_index_map, points_data_length, axes_old_index_tag_map, |
| shared_tuples_idx_map)) |
| return false; |
| compiled_byte_size += tuple.compiled_tuple_header.length + points_data_length + tuple.compiled_deltas.length; |
| } |
| return true; |
| } |
| |
| bool serialize_var_headers (hb_serialize_context_t *c, unsigned& total_header_len) const |
| { |
| TRACE_SERIALIZE (this); |
| for (const auto& tuple: tuple_vars) |
| { |
| tuple.compiled_tuple_header.as_array ().copy (c); |
| if (c->in_error ()) return_trace (false); |
| total_header_len += tuple.compiled_tuple_header.length; |
| } |
| return_trace (true); |
| } |
| |
| bool serialize_var_data (hb_serialize_context_t *c, bool is_gvar) const |
| { |
| TRACE_SERIALIZE (this); |
| if (is_gvar && shared_points_bytes) |
| { |
| hb_bytes_t s (shared_points_bytes->arrayZ, shared_points_bytes->length); |
| s.copy (c); |
| } |
| |
| for (const auto& tuple: tuple_vars) |
| { |
| const hb_vector_t<bool>* points_set = &(tuple.indices); |
| hb_vector_t<char> *point_data; |
| if (!point_data_map.has (points_set, &point_data)) |
| return_trace (false); |
| |
| if (!is_gvar || point_data != shared_points_bytes) |
| { |
| hb_bytes_t s (point_data->arrayZ, point_data->length); |
| s.copy (c); |
| } |
| |
| tuple.compiled_deltas.as_array ().copy (c); |
| if (c->in_error ()) return_trace (false); |
| } |
| |
| /* padding for gvar */ |
| if (is_gvar && (compiled_byte_size % 2)) |
| { |
| HBUINT8 pad; |
| pad = 0; |
| if (!c->embed (pad)) return_trace (false); |
| } |
| return_trace (true); |
| } |
| }; |
| |
| struct tuple_iterator_t |
| { |
| unsigned get_axis_count () const { return axis_count; } |
| |
| void init (hb_bytes_t var_data_bytes_, unsigned int axis_count_, const void *table_base_) |
| { |
| var_data_bytes = var_data_bytes_; |
| var_data = var_data_bytes_.as<TupleVariationData> (); |
| index = 0; |
| axis_count = axis_count_; |
| current_tuple = &var_data->get_tuple_var_header (); |
| data_offset = 0; |
| table_base = table_base_; |
| } |
| |
| bool get_shared_indices (hb_vector_t<unsigned int> &shared_indices /* OUT */) |
| { |
| if (var_data->has_shared_point_numbers ()) |
| { |
| const HBUINT8 *base = &(table_base+var_data->data); |
| const HBUINT8 *p = base; |
| if (!decompile_points (p, shared_indices, (const HBUINT8 *) (var_data_bytes.arrayZ + var_data_bytes.length))) return false; |
| data_offset = p - base; |
| } |
| return true; |
| } |
| |
| bool is_valid () const |
| { |
| return (index < var_data->tupleVarCount.get_count ()) && |
| var_data_bytes.check_range (current_tuple, TupleVariationHeader::min_size) && |
| var_data_bytes.check_range (current_tuple, hb_max (current_tuple->get_data_size (), |
| current_tuple->get_size (axis_count))); |
| } |
| |
| bool move_to_next () |
| { |
| data_offset += current_tuple->get_data_size (); |
| current_tuple = ¤t_tuple->get_next (axis_count); |
| index++; |
| return is_valid (); |
| } |
| |
| const HBUINT8 *get_serialized_data () const |
| { return &(table_base+var_data->data) + data_offset; } |
| |
| private: |
| const TupleVariationData *var_data; |
| unsigned int index; |
| unsigned int axis_count; |
| unsigned int data_offset; |
| const void *table_base; |
| |
| public: |
| hb_bytes_t var_data_bytes; |
| const TupleVariationHeader *current_tuple; |
| }; |
| |
| static bool get_tuple_iterator (hb_bytes_t var_data_bytes, unsigned axis_count, |
| const void *table_base, |
| hb_vector_t<unsigned int> &shared_indices /* OUT */, |
| tuple_iterator_t *iterator /* OUT */) |
| { |
| iterator->init (var_data_bytes, axis_count, table_base); |
| if (!iterator->get_shared_indices (shared_indices)) |
| return false; |
| return iterator->is_valid (); |
| } |
| |
| bool has_shared_point_numbers () const { return tupleVarCount.has_shared_point_numbers (); } |
| |
| static bool decompile_points (const HBUINT8 *&p /* IN/OUT */, |
| hb_vector_t<unsigned int> &points /* OUT */, |
| const HBUINT8 *end) |
| { |
| enum packed_point_flag_t |
| { |
| POINTS_ARE_WORDS = 0x80, |
| POINT_RUN_COUNT_MASK = 0x7F |
| }; |
| |
| if (unlikely (p + 1 > end)) return false; |
| |
| unsigned count = *p++; |
| if (count & POINTS_ARE_WORDS) |
| { |
| if (unlikely (p + 1 > end)) return false; |
| count = ((count & POINT_RUN_COUNT_MASK) << 8) | *p++; |
| } |
| if (unlikely (!points.resize (count, false))) return false; |
| |
| unsigned n = 0; |
| unsigned i = 0; |
| while (i < count) |
| { |
| if (unlikely (p + 1 > end)) return false; |
| unsigned control = *p++; |
| unsigned run_count = (control & POINT_RUN_COUNT_MASK) + 1; |
| unsigned stop = i + run_count; |
| if (unlikely (stop > count)) return false; |
| if (control & POINTS_ARE_WORDS) |
| { |
| if (unlikely (p + run_count * HBUINT16::static_size > end)) return false; |
| for (; i < stop; i++) |
| { |
| n += *(const HBUINT16 *)p; |
| points.arrayZ[i] = n; |
| p += HBUINT16::static_size; |
| } |
| } |
| else |
| { |
| if (unlikely (p + run_count > end)) return false; |
| for (; i < stop; i++) |
| { |
| n += *p++; |
| points.arrayZ[i] = n; |
| } |
| } |
| } |
| return true; |
| } |
| |
| template <typename T> |
| static bool decompile_deltas (const HBUINT8 *&p /* IN/OUT */, |
| hb_vector_t<T> &deltas /* IN/OUT */, |
| const HBUINT8 *end, |
| bool consume_all = false) |
| { |
| return TupleValues::decompile (p, deltas, end, consume_all); |
| } |
| |
| bool has_data () const { return tupleVarCount; } |
| |
| bool decompile_tuple_variations (unsigned point_count, |
| bool is_gvar, |
| tuple_iterator_t iterator, |
| const hb_map_t *axes_old_index_tag_map, |
| const hb_vector_t<unsigned> &shared_indices, |
| const hb_array_t<const F2DOT14> shared_tuples, |
| tuple_variations_t& tuple_variations, /* OUT */ |
| bool is_composite_glyph = false) const |
| { |
| return tuple_variations.create_from_tuple_var_data (iterator, tupleVarCount, |
| point_count, is_gvar, |
| axes_old_index_tag_map, |
| shared_indices, |
| shared_tuples, |
| is_composite_glyph); |
| } |
| |
| bool serialize (hb_serialize_context_t *c, |
| bool is_gvar, |
| const tuple_variations_t& tuple_variations) const |
| { |
| TRACE_SERIALIZE (this); |
| /* empty tuple variations, just return and skip serialization. */ |
| if (!tuple_variations) return_trace (true); |
| |
| auto *out = c->start_embed (this); |
| if (unlikely (!c->extend_min (out))) return_trace (false); |
| |
| if (!c->check_assign (out->tupleVarCount, tuple_variations.get_var_count (), |
| HB_SERIALIZE_ERROR_INT_OVERFLOW)) return_trace (false); |
| |
| unsigned total_header_len = 0; |
| |
| if (!tuple_variations.serialize_var_headers (c, total_header_len)) |
| return_trace (false); |
| |
| unsigned data_offset = min_size + total_header_len; |
| if (!is_gvar) data_offset += 4; |
| if (!c->check_assign (out->data, data_offset, HB_SERIALIZE_ERROR_INT_OVERFLOW)) return_trace (false); |
| |
| return tuple_variations.serialize_var_data (c, is_gvar); |
| } |
| |
| protected: |
| struct TupleVarCount : HBUINT16 |
| { |
| friend struct tuple_variations_t; |
| bool has_shared_point_numbers () const { return ((*this) & SharedPointNumbers); } |
| unsigned int get_count () const { return (*this) & CountMask; } |
| TupleVarCount& operator = (uint16_t i) { HBUINT16::operator= (i); return *this; } |
| explicit operator bool () const { return get_count (); } |
| |
| protected: |
| enum Flags |
| { |
| SharedPointNumbers= 0x8000u, |
| CountMask = 0x0FFFu |
| }; |
| public: |
| DEFINE_SIZE_STATIC (2); |
| }; |
| |
| TupleVarCount tupleVarCount; /* A packed field. The high 4 bits are flags, and the |
| * low 12 bits are the number of tuple variation tables |
| * for this glyph. The number of tuple variation tables |
| * can be any number between 1 and 4095. */ |
| Offset16To<HBUINT8> |
| data; /* Offset from the start of the base table |
| * to the serialized data. */ |
| /* TupleVariationHeader tupleVariationHeaders[] *//* Array of tuple variation headers. */ |
| public: |
| DEFINE_SIZE_MIN (4); |
| }; |
| |
| using tuple_variations_t = TupleVariationData::tuple_variations_t; |
| struct item_variations_t |
| { |
| using region_t = const hb_hashmap_t<hb_tag_t, Triple>*; |
| private: |
| /* each subtable is decompiled into a tuple_variations_t, in which all tuples |
| * have the same num of deltas (rows) */ |
| hb_vector_t<tuple_variations_t> vars; |
| |
| /* num of retained rows for each subtable, there're 2 cases when var_data is empty: |
| * 1. retained item_count is zero |
| * 2. regions is empty and item_count is non-zero. |
| * when converting to tuples, both will be dropped because the tuple is empty, |
| * however, we need to retain 2. as all-zero rows to keep original varidx |
| * valid, so we need a way to remember the num of rows for each subtable */ |
| hb_vector_t<unsigned> var_data_num_rows; |
| |
| /* original region list, decompiled from item varstore, used when rebuilding |
| * region list after instantiation */ |
| hb_vector_t<hb_hashmap_t<hb_tag_t, Triple>> orig_region_list; |
| |
| /* region list: vector of Regions, maintain the original order for the regions |
| * that existed before instantiate (), append the new regions at the end. |
| * Regions are stored in each tuple already, save pointers only. |
| * When converting back to item varstore, unused regions will be pruned */ |
| hb_vector_t<region_t> region_list; |
| |
| /* region -> idx map after instantiation and pruning unused regions */ |
| hb_hashmap_t<region_t, unsigned> region_map; |
| |
| /* all delta rows after instantiation */ |
| hb_vector_t<hb_vector_t<int>> delta_rows; |
| /* final optimized vector of encoding objects used to assemble the varstore */ |
| hb_vector_t<delta_row_encoding_t> encodings; |
| |
| /* old varidxes -> new var_idxes map */ |
| hb_map_t varidx_map; |
| |
| /* has long words */ |
| bool has_long = false; |
| |
| public: |
| bool has_long_word () const |
| { return has_long; } |
| |
| const hb_vector_t<region_t>& get_region_list () const |
| { return region_list; } |
| |
| const hb_vector_t<delta_row_encoding_t>& get_vardata_encodings () const |
| { return encodings; } |
| |
| const hb_map_t& get_varidx_map () const |
| { return varidx_map; } |
| |
| bool instantiate (const ItemVariationStore& varStore, |
| const hb_subset_plan_t *plan, |
| bool optimize=true, |
| bool use_no_variation_idx=true, |
| const hb_array_t <const hb_inc_bimap_t> inner_maps = hb_array_t<const hb_inc_bimap_t> ()) |
| { |
| if (!create_from_item_varstore (varStore, plan->axes_old_index_tag_map, inner_maps)) |
| return false; |
| if (!instantiate_tuple_vars (plan->axes_location, plan->axes_triple_distances)) |
| return false; |
| return as_item_varstore (optimize, use_no_variation_idx); |
| } |
| |
| /* keep below APIs public only for unit test: test-item-varstore */ |
| bool create_from_item_varstore (const ItemVariationStore& varStore, |
| const hb_map_t& axes_old_index_tag_map, |
| const hb_array_t <const hb_inc_bimap_t> inner_maps = hb_array_t<const hb_inc_bimap_t> ()) |
| { |
| const VarRegionList& regionList = varStore.get_region_list (); |
| if (!regionList.get_var_regions (axes_old_index_tag_map, orig_region_list)) |
| return false; |
| |
| unsigned num_var_data = varStore.get_sub_table_count (); |
| if (inner_maps && inner_maps.length != num_var_data) return false; |
| if (!vars.alloc (num_var_data) || |
| !var_data_num_rows.alloc (num_var_data)) return false; |
| |
| for (unsigned i = 0; i < num_var_data; i++) |
| { |
| if (inner_maps && !inner_maps.arrayZ[i].get_population ()) |
| continue; |
| tuple_variations_t var_data_tuples; |
| unsigned item_count = 0; |
| if (!var_data_tuples.create_from_item_var_data (varStore.get_sub_table (i), |
| orig_region_list, |
| axes_old_index_tag_map, |
| item_count, |
| inner_maps ? &(inner_maps.arrayZ[i]) : nullptr)) |
| return false; |
| |
| var_data_num_rows.push (item_count); |
| vars.push (std::move (var_data_tuples)); |
| } |
| return !vars.in_error () && !var_data_num_rows.in_error () && vars.length == var_data_num_rows.length; |
| } |
| |
| bool instantiate_tuple_vars (const hb_hashmap_t<hb_tag_t, Triple>& normalized_axes_location, |
| const hb_hashmap_t<hb_tag_t, TripleDistances>& axes_triple_distances) |
| { |
| for (tuple_variations_t& tuple_vars : vars) |
| if (!tuple_vars.instantiate (normalized_axes_location, axes_triple_distances)) |
| return false; |
| |
| if (!build_region_list ()) return false; |
| return true; |
| } |
| |
| bool build_region_list () |
| { |
| /* scan all tuples and collect all unique regions, prune unused regions */ |
| hb_hashmap_t<region_t, unsigned> all_regions; |
| hb_hashmap_t<region_t, unsigned> used_regions; |
| |
| /* use a vector when inserting new regions, make result deterministic */ |
| hb_vector_t<region_t> all_unique_regions; |
| for (const tuple_variations_t& sub_table : vars) |
| { |
| for (const tuple_delta_t& tuple : sub_table.tuple_vars) |
| { |
| region_t r = &(tuple.axis_tuples); |
| if (!used_regions.has (r)) |
| { |
| bool all_zeros = true; |
| for (float d : tuple.deltas_x) |
| { |
| int delta = (int) roundf (d); |
| if (delta != 0) |
| { |
| all_zeros = false; |
| break; |
| } |
| } |
| if (!all_zeros) |
| { |
| if (!used_regions.set (r, 1)) |
| return false; |
| } |
| } |
| if (all_regions.has (r)) |
| continue; |
| if (!all_regions.set (r, 1)) |
| return false; |
| all_unique_regions.push (r); |
| } |
| } |
| |
| if (!all_regions || !all_unique_regions) return false; |
| if (!region_list.alloc (all_regions.get_population ())) |
| return false; |
| |
| unsigned idx = 0; |
| /* append the original regions that pre-existed */ |
| for (const auto& r : orig_region_list) |
| { |
| if (!all_regions.has (&r) || !used_regions.has (&r)) |
| continue; |
| |
| region_list.push (&r); |
| if (!region_map.set (&r, idx)) |
| return false; |
| all_regions.del (&r); |
| idx++; |
| } |
| |
| /* append the new regions at the end */ |
| for (const auto& r: all_unique_regions) |
| { |
| if (!all_regions.has (r) || !used_regions.has (r)) |
| continue; |
| region_list.push (r); |
| if (!region_map.set (r, idx)) |
| return false; |
| all_regions.del (r); |
| idx++; |
| } |
| return (!region_list.in_error ()) && (!region_map.in_error ()); |
| } |
| |
| /* main algorithm ported from fonttools VarStore_optimize() method, optimize |
| * varstore by default */ |
| |
| struct combined_gain_idx_tuple_t |
| { |
| int gain; |
| unsigned idx_1; |
| unsigned idx_2; |
| |
| combined_gain_idx_tuple_t () = default; |
| combined_gain_idx_tuple_t (int gain_, unsigned i, unsigned j) |
| :gain (gain_), idx_1 (i), idx_2 (j) {} |
| |
| bool operator < (const combined_gain_idx_tuple_t& o) |
| { |
| if (gain != o.gain) |
| return gain < o.gain; |
| |
| if (idx_1 != o.idx_1) |
| return idx_1 < o.idx_1; |
| |
| return idx_2 < o.idx_2; |
| } |
| |
| bool operator <= (const combined_gain_idx_tuple_t& o) |
| { |
| if (*this < o) return true; |
| return gain == o.gain && idx_1 == o.idx_1 && idx_2 == o.idx_2; |
| } |
| }; |
| |
| bool as_item_varstore (bool optimize=true, bool use_no_variation_idx=true) |
| { |
| if (!region_list) return false; |
| unsigned num_cols = region_list.length; |
| /* pre-alloc a 2D vector for all sub_table's VarData rows */ |
| unsigned total_rows = 0; |
| for (unsigned major = 0; major < var_data_num_rows.length; major++) |
| total_rows += var_data_num_rows[major]; |
| |
| if (!delta_rows.resize (total_rows)) return false; |
| /* init all rows to [0]*num_cols */ |
| for (unsigned i = 0; i < total_rows; i++) |
| if (!(delta_rows[i].resize (num_cols))) return false; |
| |
| /* old VarIdxes -> full encoding_row mapping */ |
| hb_hashmap_t<unsigned, const hb_vector_t<int>*> front_mapping; |
| unsigned start_row = 0; |
| hb_vector_t<delta_row_encoding_t> encoding_objs; |
| hb_hashmap_t<hb_vector_t<uint8_t>, unsigned> chars_idx_map; |
| |
| /* delta_rows map, used for filtering out duplicate rows */ |
| hb_hashmap_t<const hb_vector_t<int>*, unsigned> delta_rows_map; |
| for (unsigned major = 0; major < vars.length; major++) |
| { |
| /* deltas are stored in tuples(column based), convert them back into items |
| * (row based) delta */ |
| const tuple_variations_t& tuples = vars[major]; |
| unsigned num_rows = var_data_num_rows[major]; |
| for (const tuple_delta_t& tuple: tuples.tuple_vars) |
| { |
| if (tuple.deltas_x.length != num_rows) |
| return false; |
| |
| /* skip unused regions */ |
| unsigned *col_idx; |
| if (!region_map.has (&(tuple.axis_tuples), &col_idx)) |
| continue; |
| |
| for (unsigned i = 0; i < num_rows; i++) |
| { |
| int rounded_delta = roundf (tuple.deltas_x[i]); |
| delta_rows[start_row + i][*col_idx] += rounded_delta; |
| if ((!has_long) && (rounded_delta < -65536 || rounded_delta > 65535)) |
| has_long = true; |
| } |
| } |
| |
| if (!optimize) |
| { |
| /* assemble a delta_row_encoding_t for this subtable, skip optimization so |
| * chars is not initialized, we only need delta rows for serialization */ |
| delta_row_encoding_t obj; |
| for (unsigned r = start_row; r < start_row + num_rows; r++) |
| obj.add_row (&(delta_rows.arrayZ[r])); |
| |
| encodings.push (std::move (obj)); |
| start_row += num_rows; |
| continue; |
| } |
| |
| for (unsigned minor = 0; minor < num_rows; minor++) |
| { |
| const hb_vector_t<int>& row = delta_rows[start_row + minor]; |
| if (use_no_variation_idx) |
| { |
| bool all_zeros = true; |
| for (int delta : row) |
| { |
| if (delta != 0) |
| { |
| all_zeros = false; |
| break; |
| } |
| } |
| if (all_zeros) |
| continue; |
| } |
| |
| if (!front_mapping.set ((major<<16) + minor, &row)) |
| return false; |
| |
| hb_vector_t<uint8_t> chars = delta_row_encoding_t::get_row_chars (row); |
| if (!chars) return false; |
| |
| if (delta_rows_map.has (&row)) |
| continue; |
| |
| delta_rows_map.set (&row, 1); |
| unsigned *obj_idx; |
| if (chars_idx_map.has (chars, &obj_idx)) |
| { |
| delta_row_encoding_t& obj = encoding_objs[*obj_idx]; |
| if (!obj.add_row (&row)) |
| return false; |
| } |
| else |
| { |
| if (!chars_idx_map.set (chars, encoding_objs.length)) |
| return false; |
| delta_row_encoding_t obj (std::move (chars), &row); |
| encoding_objs.push (std::move (obj)); |
| } |
| } |
| |
| start_row += num_rows; |
| } |
| |
| /* return directly if no optimization, maintain original VariationIndex so |
| * varidx_map would be empty */ |
| if (!optimize) return !encodings.in_error (); |
| |
| /* sort encoding_objs */ |
| encoding_objs.qsort (); |
| |
| /* main algorithm: repeatedly pick 2 best encodings to combine, and combine |
| * them */ |
| hb_priority_queue_t<combined_gain_idx_tuple_t> queue; |
| unsigned num_todos = encoding_objs.length; |
| for (unsigned i = 0; i < num_todos; i++) |
| { |
| for (unsigned j = i + 1; j < num_todos; j++) |
| { |
| int combining_gain = encoding_objs.arrayZ[i].gain_from_merging (encoding_objs.arrayZ[j]); |
| if (combining_gain > 0) |
| queue.insert (combined_gain_idx_tuple_t (-combining_gain, i, j), 0); |
| } |
| } |
| |
| hb_set_t removed_todo_idxes; |
| while (queue) |
| { |
| auto t = queue.pop_minimum ().first; |
| unsigned i = t.idx_1; |
| unsigned j = t.idx_2; |
| |
| if (removed_todo_idxes.has (i) || removed_todo_idxes.has (j)) |
| continue; |
| |
| delta_row_encoding_t& encoding = encoding_objs.arrayZ[i]; |
| delta_row_encoding_t& other_encoding = encoding_objs.arrayZ[j]; |
| |
| removed_todo_idxes.add (i); |
| removed_todo_idxes.add (j); |
| |
| hb_vector_t<uint8_t> combined_chars; |
| if (!combined_chars.alloc (encoding.chars.length)) |
| return false; |
| |
| for (unsigned idx = 0; idx < encoding.chars.length; idx++) |
| { |
| uint8_t v = hb_max (encoding.chars.arrayZ[idx], other_encoding.chars.arrayZ[idx]); |
| combined_chars.push (v); |
| } |
| |
| delta_row_encoding_t combined_encoding_obj (std::move (combined_chars)); |
| for (const auto& row : hb_concat (encoding.items, other_encoding.items)) |
| combined_encoding_obj.add_row (row); |
| |
| for (unsigned idx = 0; idx < encoding_objs.length; idx++) |
| { |
| if (removed_todo_idxes.has (idx)) continue; |
| |
| const delta_row_encoding_t& obj = encoding_objs.arrayZ[idx]; |
| if (obj.chars == combined_chars) |
| { |
| for (const auto& row : obj.items) |
| combined_encoding_obj.add_row (row); |
| |
| removed_todo_idxes.add (idx); |
| continue; |
| } |
| |
| int combined_gain = combined_encoding_obj.gain_from_merging (obj); |
| if (combined_gain > 0) |
| queue.insert (combined_gain_idx_tuple_t (-combined_gain, idx, encoding_objs.length), 0); |
| } |
| |
| encoding_objs.push (std::move (combined_encoding_obj)); |
| } |
| |
| int num_final_encodings = (int) encoding_objs.length - (int) removed_todo_idxes.get_population (); |
| if (num_final_encodings <= 0) return false; |
| |
| if (!encodings.alloc (num_final_encodings)) return false; |
| for (unsigned i = 0; i < encoding_objs.length; i++) |
| { |
| if (removed_todo_idxes.has (i)) continue; |
| encodings.push (std::move (encoding_objs.arrayZ[i])); |
| } |
| |
| /* sort again based on width, make result deterministic */ |
| encodings.qsort (delta_row_encoding_t::cmp_width); |
| |
| return compile_varidx_map (front_mapping); |
| } |
| |
| private: |
| /* compile varidx_map for one VarData subtable (index specified by major) */ |
| bool compile_varidx_map (const hb_hashmap_t<unsigned, const hb_vector_t<int>*>& front_mapping) |
| { |
| /* full encoding_row -> new VarIdxes mapping */ |
| hb_hashmap_t<const hb_vector_t<int>*, unsigned> back_mapping; |
| |
| for (unsigned major = 0; major < encodings.length; major++) |
| { |
| delta_row_encoding_t& encoding = encodings[major]; |
| /* just sanity check, this shouldn't happen */ |
| if (encoding.is_empty ()) |
| return false; |
| |
| unsigned num_rows = encoding.items.length; |
| |
| /* sort rows, make result deterministic */ |
| encoding.items.qsort (_cmp_row); |
| |
| /* compile old to new var_idxes mapping */ |
| for (unsigned minor = 0; minor < num_rows; minor++) |
| { |
| unsigned new_varidx = (major << 16) + minor; |
| back_mapping.set (encoding.items.arrayZ[minor], new_varidx); |
| } |
| } |
| |
| for (auto _ : front_mapping.iter ()) |
| { |
| unsigned old_varidx = _.first; |
| unsigned *new_varidx; |
| if (back_mapping.has (_.second, &new_varidx)) |
| varidx_map.set (old_varidx, *new_varidx); |
| else |
| varidx_map.set (old_varidx, HB_OT_LAYOUT_NO_VARIATIONS_INDEX); |
| } |
| return !varidx_map.in_error (); |
| } |
| |
| static int _cmp_row (const void *pa, const void *pb) |
| { |
| /* compare pointers of vectors(const hb_vector_t<int>*) that represent a row */ |
| const hb_vector_t<int>** a = (const hb_vector_t<int>**) pa; |
| const hb_vector_t<int>** b = (const hb_vector_t<int>**) pb; |
| |
| for (unsigned i = 0; i < (*b)->length; i++) |
| { |
| int va = (*a)->arrayZ[i]; |
| int vb = (*b)->arrayZ[i]; |
| if (va != vb) |
| return va < vb ? -1 : 1; |
| } |
| return 0; |
| } |
| }; |
| |
| |
| } /* namespace OT */ |
| |
| |
| #endif /* HB_OT_VAR_COMMON_HH */ |
