KleidiCV Coverage Report

Directory: ./
File: kleidicv/src/morphology/morphology_sc.h
Date: 2025-09-25 14:13:34
Exec Total Coverage
Lines: 335 335 100.0%
Functions: 110 110 100.0%
Branches: 96 96 100.0%
Line Branch Exec Source
1 // SPDX-FileCopyrightText: 2023 - 2025 Arm Limited and/or its affiliates <open-source-office@arm.com>
2 //
3 // SPDX-License-Identifier: Apache-2.0
4
5 #ifndef KLEIDICV_MORPHOLOGY_SC_H
6 #define KLEIDICV_MORPHOLOGY_SC_H
7
8 #include <algorithm>
9 #include <limits>
10
11 #include "kleidicv/kleidicv.h"
12 #include "kleidicv/morphology/workspace.h"
13 #include "kleidicv/sve2.h"
14 #include "kleidicv/types.h"
15
16 namespace KLEIDICV_TARGET_NAMESPACE {
17
18 template <typename T>
19 class CopyDataSVE2 {
20 class CopyOperation final : public UnrollTwice {
21 public:
22 using ContextType = Context;
23 using VecTraits = KLEIDICV_TARGET_NAMESPACE::VecTraits<T>;
24 using VectorType = typename VecTraits::VectorType;
25
26 4700 VectorType vector_path(ContextType, VectorType src) KLEIDICV_STREAMING {
27 4700 return src;
28 }
29 }; // end of class CopyOperation
30
31 public:
32 3714 void operator()(Rows<const T> src_rows, Rows<T> dst_rows,
33 size_t length) const KLEIDICV_STREAMING {
34 // 'apply_operation_by_rows' can only handle one channel well
35 // so width must be multiplied in order to copy all the data
36 3714 Rectangle rect{length * dst_rows.channels(), std::size_t{1}};
37 3714 Rows<const T> src_1ch{&src_rows[0], src_rows.stride(), 1};
38 3714 Rows<T> dst_1ch{&dst_rows[0], dst_rows.stride(), 1};
39 3714 CopyOperation op{};
40 3714 apply_operation_by_rows(op, rect, src_1ch, dst_1ch);
41 3714 }
42 };
43
44 template <typename ScalarType, typename O>
45 class VerticalOp final {
46 public:
47 using VecTraits = KLEIDICV_TARGET_NAMESPACE::VecTraits<ScalarType>;
48
49 796 VerticalOp(Rectangle rect, Rectangle kernel) KLEIDICV_STREAMING
50 796 : rect_(rect),
51 796 kernel_(kernel) {}
52
53 796 void process_rows(IndirectRows<ScalarType> src_rows,
54 Rows<ScalarType> dst_rows) KLEIDICV_STREAMING {
55
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 796 if (KLEIDICV_UNLIKELY(kernel_.height()) == 1) {
56 64 CopyRows<ScalarType>::copy_rows(rect_, src_rows, dst_rows);
57 64 return;
58 }
59
60 // Iterate across the rows from top to bottom. This implementation can
61 // handle two rows at once.
62
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 3050 for (size_t height = 0; height < rect_.height(); height += 2) {
63 // Iterate across the columns from left to right.
64 4636 LoopUnroll2 loop{rect_.width() * src_rows.channels(),
65 2318 VecTraits::num_lanes()};
66 // clang-format off
67 2366 loop.unroll_four_times([&](size_t index) KLEIDICV_STREAMING {
68 48 vector_path_4x(src_rows, dst_rows, index, height);
69 48 })
70 2378 .unroll_twice([&](size_t index) KLEIDICV_STREAMING {
71 60 vector_path_2x(src_rows, dst_rows, index, height);
72 60 })
73 4636 .remaining([&](size_t index, size_t length) KLEIDICV_STREAMING {
74 2318 svbool_t pg = VecTraits::svwhilelt(index, length);
75
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 4988 while (svptest_first(VecTraits::svptrue(), pg)) {
76 2670 vector_path(pg, src_rows, dst_rows, index, height);
77 2670 index += VecTraits::num_lanes();
78 2670 pg = VecTraits::svwhilelt(index, length);
79 }
80 2318 });
81 // clang-format on
82 2318 src_rows += 2;
83 2318 dst_rows += 2;
84 2318 }
85 796 }
86
87 private:
88 48 void vector_path_4x(IndirectRows<ScalarType> src_rows,
89 Rows<ScalarType> dst_rows, const size_t index,
90 const size_t height) KLEIDICV_STREAMING {
91 48 const ScalarType *src_row = &src_rows[index];
92 48 auto first_row0 = svld1(VecTraits::svptrue(), &src_row[0]);
93 48 auto first_row1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
94 48 auto first_row2 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 2);
95 48 auto first_row3 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 3);
96 48 ++src_rows;
97
98 48 src_row = &src_rows[index];
99 48 auto acc0 = svld1(VecTraits::svptrue(), &src_row[0]);
100 48 auto acc1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
101 48 auto acc2 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 2);
102 48 auto acc3 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 3);
103 48 ++src_rows;
104
105 48 LoopUnroll loop{kernel_.height() - 2, 2};
106
107 72 loop.unroll_once([&](size_t step) KLEIDICV_STREAMING {
108 24 const ScalarType *src_row0 = &src_rows.at(0)[index];
109 24 const ScalarType *src_row1 = &src_rows.at(1)[index];
110 24 auto row00 = svld1(VecTraits::svptrue(), src_row0);
111 24 auto row01 = svld1_vnum(VecTraits::svptrue(), src_row0, 1);
112 24 auto row02 = svld1_vnum(VecTraits::svptrue(), src_row0, 2);
113 24 auto row03 = svld1_vnum(VecTraits::svptrue(), src_row0, 3);
114 24 auto row10 = svld1(VecTraits::svptrue(), src_row1);
115 24 auto row11 = svld1_vnum(VecTraits::svptrue(), src_row1, 1);
116 24 auto row12 = svld1_vnum(VecTraits::svptrue(), src_row1, 2);
117 24 auto row13 = svld1_vnum(VecTraits::svptrue(), src_row1, 3);
118 48 acc0 = O::operation(VecTraits::svptrue(), acc0,
119 24 O::operation(VecTraits::svptrue(), row00, row10));
120 48 acc1 = O::operation(VecTraits::svptrue(), acc1,
121 24 O::operation(VecTraits::svptrue(), row01, row11));
122 48 acc2 = O::operation(VecTraits::svptrue(), acc2,
123 24 O::operation(VecTraits::svptrue(), row02, row12));
124 48 acc3 = O::operation(VecTraits::svptrue(), acc3,
125 24 O::operation(VecTraits::svptrue(), row03, row13));
126 24 src_rows += step;
127 24 });
128
129 88 loop.tail([&](size_t /* index */) // NOLINT(readability/casting)
130 KLEIDICV_STREAMING {
131 40 const ScalarType *src_row = &src_rows[index];
132 40 auto row0 = svld1(VecTraits::svptrue(), &src_row[0]);
133 40 auto row1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
134 40 auto row2 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 2);
135 40 auto row3 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 3);
136 40 acc0 = O::operation(VecTraits::svptrue(), acc0, row0);
137 40 acc1 = O::operation(VecTraits::svptrue(), acc1, row1);
138 40 acc2 = O::operation(VecTraits::svptrue(), acc2, row2);
139 40 acc3 = O::operation(VecTraits::svptrue(), acc3, row3);
140 40 ++src_rows;
141 40 });
142
143 // Save partial results which do not contain the first row.
144 48 auto partial_acc0 = acc0;
145 48 auto partial_acc1 = acc1;
146 48 auto partial_acc2 = acc2;
147 48 auto partial_acc3 = acc3;
148
149 // Take the first row into account.
150 48 acc0 = O::operation(VecTraits::svptrue(), acc0, first_row0);
151 48 acc1 = O::operation(VecTraits::svptrue(), acc1, first_row1);
152 48 acc2 = O::operation(VecTraits::svptrue(), acc2, first_row2);
153 48 acc3 = O::operation(VecTraits::svptrue(), acc3, first_row3);
154
155 // Store the results.
156 48 ScalarType *dst_row = &dst_rows[index];
157 48 svst1(VecTraits::svptrue(), &dst_row[0], acc0);
158 48 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 1, acc1);
159 48 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 2, acc2);
160 48 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 3, acc3);
161
162 // Try to process one more row, because it is relatively cheap to do so.
163
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 48 if (KLEIDICV_UNLIKELY((height + 1) >= rect_.height())) {
164 16 return;
165 }
166
167 32 ++dst_rows;
168
169 32 src_row = &src_rows[index];
170 32 auto next_row0 = svld1(VecTraits::svptrue(), &src_row[0]);
171 32 auto next_row1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
172 32 auto next_row2 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 2);
173 32 auto next_row3 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 3);
174
175 32 acc0 = O::operation(VecTraits::svptrue(), partial_acc0, next_row0);
176 32 acc1 = O::operation(VecTraits::svptrue(), partial_acc1, next_row1);
177 32 acc2 = O::operation(VecTraits::svptrue(), partial_acc2, next_row2);
178 32 acc3 = O::operation(VecTraits::svptrue(), partial_acc3, next_row3);
179
180 // Store the results.
181 32 dst_row = &dst_rows[index];
182 32 svst1(VecTraits::svptrue(), &dst_row[0], acc0);
183 32 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 1, acc1);
184 32 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 2, acc2);
185 32 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 3, acc3);
186 48 }
187
188 60 void vector_path_2x(IndirectRows<ScalarType> src_rows,
189 Rows<ScalarType> dst_rows, const size_t index,
190 const size_t height) KLEIDICV_STREAMING {
191 60 const ScalarType *src_row = &src_rows[index];
192 60 auto first_row0 = svld1(VecTraits::svptrue(), &src_row[0]);
193 60 auto first_row1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
194 60 ++src_rows;
195
196 60 src_row = &src_rows[index];
197 60 auto acc0 = svld1(VecTraits::svptrue(), &src_row[0]);
198 60 auto acc1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
199 60 ++src_rows;
200
201 60 LoopUnroll loop{kernel_.height() - 2, 2};
202
203 88 loop.unroll_once([&](size_t step) KLEIDICV_STREAMING {
204 28 const ScalarType *src_row0 = &src_rows.at(0)[index];
205 28 const ScalarType *src_row1 = &src_rows.at(1)[index];
206 28 auto row00 = svld1(VecTraits::svptrue(), src_row0);
207 28 auto row01 = svld1_vnum(VecTraits::svptrue(), src_row0, 1);
208 28 auto row10 = svld1(VecTraits::svptrue(), src_row1);
209 28 auto row11 = svld1_vnum(VecTraits::svptrue(), src_row1, 1);
210 56 acc0 = O::operation(VecTraits::svptrue(), acc0,
211 28 O::operation(VecTraits::svptrue(), row00, row10));
212 56 acc1 = O::operation(VecTraits::svptrue(), acc1,
213 28 O::operation(VecTraits::svptrue(), row01, row11));
214 28 src_rows += step;
215 28 });
216
217 100 loop.tail([&](size_t /* index */) // NOLINT(readability/casting)
218 KLEIDICV_STREAMING {
219 40 const ScalarType *src_row = &src_rows[index];
220 40 auto row0 = svld1(VecTraits::svptrue(), &src_row[0]);
221 40 auto row1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
222 40 acc0 = O::operation(VecTraits::svptrue(), acc0, row0);
223 40 acc1 = O::operation(VecTraits::svptrue(), acc1, row1);
224 40 ++src_rows;
225 40 });
226
227 // Save partial results which do not contain the first row.
228 60 auto partial_acc0 = acc0;
229 60 auto partial_acc1 = acc1;
230
231 // Take the first row into account.
232 60 acc0 = O::operation(VecTraits::svptrue(), acc0, first_row0);
233 60 acc1 = O::operation(VecTraits::svptrue(), acc1, first_row1);
234
235 // Store the results.
236 60 ScalarType *dst_row = &dst_rows[index];
237 60 svst1(VecTraits::svptrue(), &dst_row[0], acc0);
238 60 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 1, acc1);
239
240 // Try to process one more row, because it is relatively cheap to do so.
241
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 60 if (KLEIDICV_UNLIKELY((height + 1) >= rect_.height())) {
242 10 return;
243 }
244
245 50 ++dst_rows;
246
247 50 src_row = &src_rows[index];
248 50 auto next_row0 = svld1(VecTraits::svptrue(), &src_row[0]);
249 50 auto next_row1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
250
251 50 acc0 = O::operation(VecTraits::svptrue(), partial_acc0, next_row0);
252 50 acc1 = O::operation(VecTraits::svptrue(), partial_acc1, next_row1);
253
254 50 dst_row = &dst_rows[index];
255 50 svst1(VecTraits::svptrue(), &dst_row[0], acc0);
256 50 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 1, acc1);
257 60 }
258
259 2670 void vector_path(svbool_t pg, IndirectRows<ScalarType> src_rows,
260 Rows<ScalarType> dst_rows, const size_t index,
261 const size_t height) KLEIDICV_STREAMING {
262 2670 auto first_row = svld1(pg, &src_rows[index]);
263 2670 ++src_rows;
264
265 2670 auto acc = svld1(pg, &src_rows[index]);
266 2670 ++src_rows;
267
268 2670 LoopUnroll loop{kernel_.height() - 2, 2};
269
270 4526 loop.unroll_once([&](size_t step) KLEIDICV_STREAMING {
271 1856 auto row0 = svld1(pg, &src_rows.at(0)[index]);
272 1856 auto row1 = svld1(pg, &src_rows.at(1)[index]);
273 1856 acc = O::operation(pg, acc, O::operation(pg, row0, row1));
274 1856 src_rows += step;
275 1856 });
276
277 4736 loop.tail([&](size_t /* index */) // NOLINT(readability/casting)
278 KLEIDICV_STREAMING {
279 2066 auto row = svld1(pg, &src_rows[index]);
280 2066 acc = O::operation(pg, acc, row);
281 2066 ++src_rows;
282 2066 });
283
284 // Save partial result which does not contain the first row.
285 2670 auto partial_acc = acc;
286
287 // Take the first row into account.
288 2670 acc = O::operation(pg, acc, first_row);
289
290 // Store the results.
291 2670 svst1(pg, &dst_rows[index], acc);
292
293 // Try to process one more row, because it is relatively cheap to do so.
294
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 2670 if (KLEIDICV_UNLIKELY((height + 1) >= rect_.height())) {
295 277 return;
296 }
297
298 2393 ++dst_rows;
299
300 2393 auto next_row = svld1(pg, &src_rows[index]);
301 2393 acc = O::operation(pg, partial_acc, next_row);
302 2393 svst1(pg, &dst_rows[index], acc);
303 2670 }
304
305 Rectangle rect_;
306 Rectangle kernel_;
307 }; // end of class VerticalOp<ScalarType, )>
308
309 template <typename ScalarType, typename O>
310 class HorizontalOp final {
311 public:
312 using VecTraits = KLEIDICV_TARGET_NAMESPACE::VecTraits<ScalarType>;
313
314 6596 HorizontalOp(Rectangle rect, Rectangle kernel) KLEIDICV_STREAMING
315 6596 : rect_(rect),
316 6596 kernel_(kernel) {}
317
318 6596 void process_rows(Rows<const ScalarType> src_rows,
319 Rows<ScalarType> dst_rows) KLEIDICV_STREAMING {
320 // Iterate across the rows from top to bottom.
321
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 13192 for (size_t height = 0; height < rect_.height(); ++height) {
322 // Iterate across the columns from left to right.
323 13192 LoopUnroll2 loop{rect_.width() * src_rows.channels(),
324 6596 VecTraits::num_lanes()};
325 // clang-format off
326 6732 loop.unroll_four_times([&](size_t index) KLEIDICV_STREAMING {
327 136 vector_path_4x(src_rows, dst_rows, index);
328 136 })
329 6768 .unroll_twice([&](size_t index) KLEIDICV_STREAMING {
330 172 vector_path_2x(src_rows, dst_rows, index);
331 172 })
332 13192 .remaining([&](size_t index, size_t length) KLEIDICV_STREAMING {
333 6596 svbool_t pg = VecTraits::svwhilelt(index, length);
334
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 14478 while (svptest_first(VecTraits::svptrue(), pg)) {
335 7882 vector_path(pg, src_rows, dst_rows, index);
336 7882 index += VecTraits::num_lanes();
337 7882 pg = VecTraits::svwhilelt(index, length);
338 }
339 6596 });
340 // clang-format on
341 6596 ++src_rows;
342 6596 ++dst_rows;
343 6596 }
344 6596 }
345
346 private:
347 136 void vector_path_4x(Rows<const ScalarType> src_rows,
348 Rows<ScalarType> dst_rows,
349 const size_t index) KLEIDICV_STREAMING {
350 136 const auto *src_row = &src_rows[index];
351 136 auto acc0 = svld1(VecTraits::svptrue(), &src_row[0]);
352 136 auto acc1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
353 136 auto acc2 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 2);
354 136 auto acc3 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 3);
355
356
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 504 for (size_t width = 1; width < kernel_.width(); ++width) {
357 368 src_row = &src_rows[index + width * src_rows.channels()];
358 368 auto row0 = svld1(VecTraits::svptrue(), &src_row[0]);
359 368 auto row1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
360 368 auto row2 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 2);
361 368 auto row3 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 3);
362 368 acc0 = O::operation(VecTraits::svptrue(), acc0, row0);
363 368 acc1 = O::operation(VecTraits::svptrue(), acc1, row1);
364 368 acc2 = O::operation(VecTraits::svptrue(), acc2, row2);
365 368 acc3 = O::operation(VecTraits::svptrue(), acc3, row3);
366 368 }
367
368 136 auto dst_row = &dst_rows[index];
369 136 svst1(VecTraits::svptrue(), &dst_row[0], acc0);
370 136 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 1, acc1);
371 136 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 2, acc2);
372 136 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 3, acc3);
373 136 }
374
375 172 void vector_path_2x(Rows<const ScalarType> src_rows,
376 Rows<ScalarType> dst_rows,
377 const size_t index) KLEIDICV_STREAMING {
378 172 const auto *src_row = &src_rows[index];
379 172 auto acc0 = svld1(VecTraits::svptrue(), &src_row[0]);
380 172 auto acc1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
381
382
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 720 for (size_t width = 1; width < kernel_.width(); ++width) {
383 548 src_row = &src_rows[index + width * src_rows.channels()];
384 548 auto row0 = svld1(VecTraits::svptrue(), &src_row[0]);
385 548 auto row1 = svld1_vnum(VecTraits::svptrue(), &src_row[0], 1);
386 548 acc0 = O::operation(VecTraits::svptrue(), acc0, row0);
387 548 acc1 = O::operation(VecTraits::svptrue(), acc1, row1);
388 548 }
389
390 172 auto dst_row = &dst_rows[index];
391 172 svst1(VecTraits::svptrue(), &dst_row[0], acc0);
392 172 svst1_vnum(VecTraits::svptrue(), &dst_row[0], 1, acc1);
393 172 }
394
395 7882 void vector_path(svbool_t pg, Rows<const ScalarType> src_rows,
396 Rows<ScalarType> dst_rows,
397 const size_t index) KLEIDICV_STREAMING {
398 7882 auto acc = svld1(pg, &src_rows[index]);
399
400
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 29018 for (size_t width = 1; width < kernel_.width(); ++width) {
401 21136 const auto *src_row = &src_rows[index + width * src_rows.channels()];
402 21136 acc = O::operation(pg, acc, svld1(pg, &src_row[0]));
403 21136 }
404
405 7882 svst1(pg, &dst_rows[index], acc);
406 7882 }
407
408 Rectangle rect_;
409 Rectangle kernel_;
410 }; // end of class HorizontalOp<ScalarType>
411
412 template <typename ScalarType>
413 class Min final {
414 public:
415 using VecTraits = KLEIDICV_TARGET_NAMESPACE::VecTraits<ScalarType>;
416 using VectorType = typename VecTraits::VectorType;
417
418 16617 static VectorType operation(svbool_t pg, VectorType lhs,
419 VectorType rhs) KLEIDICV_STREAMING {
420 16617 return svmin_x(pg, lhs, rhs);
421 }
422 }; // end of class Min<ScalarType>
423
424 template <typename ScalarType>
425 class Max final {
426 public:
427 using VecTraits = KLEIDICV_TARGET_NAMESPACE::VecTraits<ScalarType>;
428 using VectorType = typename VecTraits::VectorType;
429
430 19012 static VectorType operation(svbool_t pg, VectorType lhs,
431 VectorType rhs) KLEIDICV_STREAMING {
432 19012 return svmax_x(pg, lhs, rhs);
433 }
434 }; // end of class Max<ScalarType>
435
436 template <typename T>
437 using VerticalMin = VerticalOp<T, Min<T>>;
438 template <typename T>
439 using VerticalMax = VerticalOp<T, Max<T>>;
440
441 template <typename T>
442 using HorizontalMin = HorizontalOp<T, Min<T>>;
443 template <typename T>
444 using HorizontalMax = HorizontalOp<T, Max<T>>;
445
446 template <typename ScalarType, typename CopyDataOperation>
447 class DilateOperation final {
448 public:
449 using SourceType = ScalarType;
450 using BufferType = ScalarType;
451 using DestinationType = ScalarType;
452 using CopyData = CopyDataOperation;
453
454 408 explicit DilateOperation(Rectangle kernel) KLEIDICV_STREAMING
455 408 : kernel_{kernel} {}
456
457 3734 void process_horizontal(Rectangle rect, Rows<const SourceType> src_rows,
458 Rows<BufferType> dst_rows) KLEIDICV_STREAMING {
459 3734 HorizontalMax<ScalarType>{rect, kernel_}.process_rows(src_rows, dst_rows);
460 3734 }
461
462 430 void process_vertical(Rectangle rect, IndirectRows<BufferType> src_rows,
463 Rows<DestinationType> dst_rows) KLEIDICV_STREAMING {
464 430 VerticalMax<ScalarType>{rect, kernel_}.process_rows(src_rows, dst_rows);
465 430 }
466
467 private:
468 Rectangle kernel_;
469 }; // end of class DilateOperation<ScalarType>
470
471 template <typename T, typename CopyOperation>
472 372 static kleidicv_error_t dilate_sc(
473 const T *src, size_t src_stride, T *dst, size_t dst_stride, size_t width,
474 size_t height, kleidicv_morphology_context_t *context) KLEIDICV_STREAMING {
475
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 372 CHECK_POINTERS(context);
476
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 370 CHECK_POINTER_AND_STRIDE(src, src_stride, height);
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 368 CHECK_POINTER_AND_STRIDE(dst, dst_stride, height);
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 366 CHECK_IMAGE_SIZE(width, height);
479
480 362 auto *workspace = reinterpret_cast<MorphologyWorkspace *>(context);
481
482
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 362 if (workspace->type_size() != sizeof(T)) {
483 2 return KLEIDICV_ERROR_CONTEXT_MISMATCH;
484 }
485
486 360 Rectangle rect{width, height};
487
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 360 if (workspace->image_size() != rect) {
488 4 return KLEIDICV_ERROR_CONTEXT_MISMATCH;
489 }
490
491 // Currently valid, will need to be changed if morphology supports more border
492 // types, like KLEIDICV_BORDER_TYPE_REVERSE.
493 356 Rectangle kernel{workspace->kernel()};
494
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 356 if (width < kernel.width() - 1 || height < kernel.height() - 1) {
495 12 return KLEIDICV_ERROR_NOT_IMPLEMENTED;
496 }
497
498 344 Rows<const T> src_rows{src, src_stride, workspace->channels()};
499 344 Rows<T> dst_rows{dst, dst_stride, workspace->channels()};
500 344 Margin margin{workspace->kernel(), workspace->anchor()};
501
502 344 Rows<const T> current_src_rows = src_rows;
503 344 Rows<T> current_dst_rows = dst_rows;
504
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 752 for (size_t iteration = 0; iteration < workspace->iterations(); ++iteration) {
505 408 DilateOperation<T, CopyOperation> operation{kernel};
506 816 workspace->process(rect, current_src_rows, current_dst_rows, margin,
507 408 workspace->border_type(), operation);
508 // Update source for the next iteration.
509 408 current_src_rows = dst_rows;
510 408 }
511 344 return KLEIDICV_OK;
512 372 }
513
514 // Helper structure for erode.
515 template <typename ScalarType, typename CopyDataOperation>
516 class ErodeOperation final {
517 public:
518 using SourceType = ScalarType;
519 using BufferType = ScalarType;
520 using DestinationType = ScalarType;
521 using CopyData = CopyDataOperation;
522
523 344 explicit ErodeOperation(Rectangle kernel) KLEIDICV_STREAMING
524 344 : kernel_{kernel} {}
525
526 2862 void process_horizontal(Rectangle rect, Rows<const SourceType> src_rows,
527 Rows<BufferType> dst_rows) KLEIDICV_STREAMING {
528 2862 HorizontalMin<ScalarType>{rect, kernel_}.process_rows(src_rows, dst_rows);
529 2862 }
530
531 366 void process_vertical(Rectangle rect, IndirectRows<BufferType> src_rows,
532 Rows<DestinationType> dst_rows) KLEIDICV_STREAMING {
533 366 VerticalMin<ScalarType>{rect, kernel_}.process_rows(src_rows, dst_rows);
534 366 }
535
536 private:
537 Rectangle kernel_;
538 }; // end of class ErodeOperation<ScalarType>
539
540 template <typename T, typename CopyOperation>
541 340 static kleidicv_error_t erode_sc(
542 const T *src, size_t src_stride, T *dst, size_t dst_stride, size_t width,
543 size_t height, kleidicv_morphology_context_t *context) KLEIDICV_STREAMING {
544
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 340 CHECK_POINTERS(context);
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 338 CHECK_POINTER_AND_STRIDE(src, src_stride, height);
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 336 CHECK_POINTER_AND_STRIDE(dst, dst_stride, height);
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 334 CHECK_IMAGE_SIZE(width, height);
548
549 330 auto *workspace = reinterpret_cast<MorphologyWorkspace *>(context);
550
551
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 330 if (workspace->type_size() != sizeof(T)) {
552 2 return KLEIDICV_ERROR_CONTEXT_MISMATCH;
553 }
554
555 328 Rectangle rect{width, height};
556
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 328 if (workspace->image_size() != rect) {
557 4 return KLEIDICV_ERROR_CONTEXT_MISMATCH;
558 }
559
560 // Currently valid, will need to be changed if morphology supports more border
561 // types, like KLEIDICV_BORDER_TYPE_REVERSE.
562 324 Rectangle kernel{workspace->kernel()};
563
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 324 if (width < kernel.width() - 1 || height < kernel.height() - 1) {
564 12 return KLEIDICV_ERROR_NOT_IMPLEMENTED;
565 }
566
567 312 Rows<const T> src_rows{src, src_stride, workspace->channels()};
568 312 Rows<T> dst_rows{dst, dst_stride, workspace->channels()};
569 312 Margin margin{workspace->kernel(), workspace->anchor()};
570
571 312 Rows<const T> current_src_rows = src_rows;
572 312 Rows<T> current_dst_rows = dst_rows;
573
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 656 for (size_t iteration = 0; iteration < workspace->iterations(); ++iteration) {
574 344 ErodeOperation<T, CopyOperation> operation{kernel};
575 688 workspace->process(rect, current_src_rows, current_dst_rows, margin,
576 344 workspace->border_type(), operation);
577 // Update source for the next iteration.
578 344 current_src_rows = dst_rows;
579 344 }
580 312 return KLEIDICV_OK;
581 340 }
582
583 } // namespace KLEIDICV_TARGET_NAMESPACE
584
585 #endif // KLEIDICV_MORPHOLOGY_SC_H
586