Added harfbuzz; use it instead of STB to render text #29300
Merge with https://github.com/opencv/opencv_extra/pull/1378.
This is the next big step to improve font rendering in OpenCV 5.x. See #18760, #26301. See also OpenCV 5.0 release notes, where it was pointed out that complex scripts are not rendered correctly, and HarfBuzz integration is needed to fix it. So, here it is — HarfBuzz integration:
* Removed tweaked STB engine. STB truetype rendering engine was included into OpenCV 5.0-pre/5.0 and it was extended to instantiate and render variable fonts, but the support was incomplete and immature.
* Instead, we now use [HarfBuzz](https://github.com/harfbuzz/harfbuzz) — famous font shaping library and now also font rendering library, used by many big companies and organizations.
* As a result, we now correctly render complex scripts, such as arabic or devanagari, correctly handle font ligatures (ff, fi, ft etc.)
<img width="1300" height="600" alt="text_test" src="https://github.com/user-attachments/assets/a7d2c754-0fcf-40f8-8104-578f3a14850b" />
* Potentially, we could also support color emoji, but that would be a next step.
* As with STB-based engine, glyphs are cached (the same glyph from the same font is not rendered twice), so the performance should be on par with the previous engine.
* When OpenCV is built with `-DWITH_HARFBUZZ=ON`, which is set by default, it tries to detect HarfBuzz in the system and use it. If it's not detected, OpenCV builds our own small subset of HarfBuzz.
The following table compares size of libopencv_imgproc.dylib.5.0.0 (Release mode) in different configurations:
| Configuration | libopencv_imgproc (stripped) | delta vs 5.0 |
|----------------------------------------|------------------------------|----------|
| imgproc without text rendering (`-DWITH_HARFBUZZ=OFF`) | 4.27 MB | −2.35 MB |
| imgproc with stb-based engine (5.0) | 6.62 MB | 0.0 Mb |
| imgproc with HarfBuzz-based engine (this PR) | 7.07 MB | +0.45 MB |
The biggest source of the size increase when OpenCV is built with text rendering support (STB- or Harfbuzz-based) is that imgproc in this case includes .ttf fonts (Rubik and 'Wen Quan Yi Micro Hei'), which are gzip-compressed, but still have noticeable size, especially 'Wen Quan Yi' (~2Mb). HarfBuzz itself, compared to STB engine, adds just 0.45Mb, or ~7% to imgproc size. On other platforms (Linux x64, Windows), where IPP or other acceleration libraries are linked into libopencv_imgproc, the harfbuzz footprint is even less noticeable.
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imgproc: fix INTER_NEAREST_EXACT to match Pillow (5.x retarget of #28661) #28873
## Summary
Fixes `INTER_NEAREST_EXACT` producing different results from Pillow for images with dimensions that are multiples of 64 (e.g., 128, 192).
Retargets #28661 to 5.x per core team request.
## Root cause
The old 16-bit fixed-point arithmetic (`ifx`, `ifx0`, `>> 16`) rounds differently from Pillow's pixel-center coordinate mapping. At exact pixel boundaries (e.g., y=7 with 128 to 160: `0.4 + 7*0.8 = 5.999999999999999`), Pillow's truncation drops to 5 while the old opencv formula rounds to 6.
## Changes
- `resize.cpp`: Replace `ifx/ifx0/ify/ify0` 16-bit fixed-point with an int64 arithmetic formula equivalent to `floor((i + 0.5) * src / dst)`, computed as `((int64_t)(i * 2 + 1) * src) / (dst * 2)`. This matches Pillow's pixel-center mapping exactly, is deterministic across platforms, and avoids FP rounding divergence entirely.
- `test_resize_bitexact.cpp`: Add `NearestExact_PillowCompat` covering 4 dimension pairs including the reproducer from #28429.
## Testing
- All 3 `Resize_Bitexact` tests pass (Linear8U, Nearest8U, NearestExact_PillowCompat) on this branch built against 5.x
- Verified bit-exact match to PIL output (Python 3.14, Pillow) on 49 dimension combinations including random pairs
- Built on macOS ARM64 with HAL (carotene/KleidiCV) active
Fixes#28429
Inverted imgproc-geometry dependency and moved more functions to geometry #29230
Fixes: https://github.com/opencv/opencv/issues/20267
Continues: https://github.com/opencv/opencv/pull/29175
OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4137
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1377
Summary:
- LSD returned back to imgproc
- drawing functions moved to imgproc
- undistort image and related perf-pixel functions moved to imgproc
- moments moved to geometry
- estimateXXXtransform moved to geometry
After the patch the geometry module depends on code and Flann and may be used everywhere without potential circular dependencies
### Pull Request Readiness Checklist
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Revised bicubic interpolation kernels and updated the corresponding functions #29165
They should work more accurately and faster than the existing implementation. The PR follows the previous work (#26271 etc.) that brought faster bilinear kernels.
### Pull Request Readiness Checklist
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Moved geometry transformations from imgproc to 3d, future geometry module #29101
The first step of 2d geometry operations migration to the future geometry module.
I created 2d.hpp to isolate the moved functions for now. I propose to create geometry.hpp when the module is renamed and include all things there.
OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4126
### Pull Request Readiness Checklist
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### Summary
This PR improves SIMD coverage for masked `cv::accumulate()` on 4-channel images.
The existing masked accumulate SIMD implementation has special paths for `cn == 1` and `cn == 3`, while 4-channel images fall back to the general implementation. This PR adds `cn == 4` SIMD paths using OpenCV Universal Intrinsics.
Compare link:
https://github.com/opencv/opencv/compare/4.x...feitianduowen:opencv:4.x
Modified files:
- `modules/imgproc/src/accum.simd.hpp`
- Add masked `cn == 4` SIMD paths for:
- `CV_32FC4 -> CV_32FC4`
- `CV_8UC4 -> CV_32FC4`
- `modules/video/test/test_accum.cpp`
- Add deterministic correctness regression tests for the new masked 4-channel accumulate paths.
- `modules/imgproc/perf/perf_accumulate.cpp`
- Add performance coverage for masked 4-channel accumulate.
imgproc: add SIMD paths for masked 4-channel accumulate #29094
### Implementation
The new SIMD branches are added in:
- `modules/imgproc/src/accum.simd.hpp`
They handle:
- `src`: `CV_32FC4`, `dst`: `CV_32FC4`, `mask`: `CV_8UC1`
- `src`: `CV_8UC4`, `dst`: `CV_32FC4`, `mask`: `CV_8UC1`
For the `CV_32FC4` path, the implementation uses `v_load_deinterleave`, applies the pixel mask to all 4 channels, accumulates into `dst`, and stores the result with `v_store_interleave`.
For the `CV_8UC4 -> CV_32FC4` path, the implementation loads 4 interleaved uchar channels, applies the pixel mask, widens the values to float, accumulates into `dst`, and stores the results back as interleaved `CV_32FC4`.
The masked SIMD block is guarded with:
```cpp
if (x <= len - cVectorWidth)
{
...
}
```
This check ensures that SIMD setup and vector operations are only executed when at least one full vector iteration can run. For very small inputs or tail-only cases, the function skips SIMD initialization and lets the existing scalar fallback handle the data through `acc_general_(src, dst, mask, len, cn, x)`.
This keeps tail handling unchanged and avoids unnecessary SIMD initialization when the vector loop would be skipped.
### Accuracy tests
Added a deterministic regression test in:
* `modules/video/test/test_accum.cpp`
New test:
```bash
Video_Acc.accuracy_32FC4_masked_cn4
Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
```
Test commands:
```bash
opencv_test_video --gtest_filter=Video_Acc.accuracy_32FC4_masked_cn4
opencv_test_video --gtest_filter=Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
opencv_test_video --gtest_filter="Video_Acc.*:Video_AccSquared.*:Video_AccProduct.*:Video_RunningAvg.*"
```
The filtered accumulate-related tests passed locally.
I added dedicated deterministic tests instead of only extending the existing randomized accumulate tests because this PR only changes the masked `cv::accumulate()` paths for specific 4-channel type combinations. It does not add `cn == 4` SIMD coverage for `accumulateSquare`, `accumulateProduct`, or `accumulateWeighted`.
The existing base accumulate tests are shared by several accumulation functions. Extending the common randomized channel selection to include `cn == 4` would also affect tests for functions that are not optimized by this PR. The new deterministic tests directly cover the modified paths:
* `cv::accumulate(src, dst, mask)`
* `CV_32FC4 -> CV_32FC4`
* `CV_8UC4 -> CV_32FC4`
* `mask`: `CV_8UC1`
The test also covers small sizes and non-vector-multiple sizes, so both the new SIMD path and the existing scalar tail fallback are exercised.
### Performance tests
Added performance coverage in:
* `modules/imgproc/perf/perf_accumulate.cpp`
Perf command:
```bash
opencv_perf_imgproc --gtest_filter="*AccumulateMask32FC4*" --perf_min_samples=1000 --perf_force_samples=1000
opencv_perf_imgproc --gtest_filter="*AccumulateMask8UC4To32FC4*" --perf_min_samples=1000 --perf_force_samples=1000
```
Test environment:
* Release build
* AVX2 enabled
* IPP disabled
* OpenCL disabled
* Windows MinGW build
Performance results:
`CV_32FC4 -> CV_32FC4`
| Size | Before median | After median | Speedup |
| --------- | ------------- | ------------ | ------- |
| 1920x1080 | 3.07 ms | 2.74 ms | 1.12x |
| 1280x720 | 0.84 ms | 0.73 ms | 1.15x |
| 640x480 | 0.17 ms | 0.16 ms | 1.06x |
| 320x240 | 0.04 ms | 0.04 ms | ~1.00x |
`CV_8UC4 -> CV_32FC4`
| Size | Before median | After median | Speedup |
| --------- | ------------- | ------------ | ------- |
| 1920x1080 | 6.09 ms | 1.75 ms | 3.48x |
| 1280x720 | 2.69 ms | 0.82 ms | 3.28x |
| 640x480 | 0.96 ms | 0.28 ms | 3.43x |
| 320x240 | 0.24 ms | 0.06 ms | 4.00x |
| 127x61 | 0.02 ms | 0.01 ms | 2.00x |
### Build and test commands
The following commands were run from Windows `cmd.exe`.
```sh
cmake -S . -B build_release -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON -DBUILD_PERF_TESTS=OFF -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_release --target opencv_test_video -j 4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.accuracy_32FC4_masked_cn4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.*:Video_AccSquared.*:Video_AccProduct.*:Video_RunningAvg.*
```
Accuracy test commands passed locally.
For the performance comparison, I kept the new perf test in `modules/imgproc/perf/perf_accumulate.cpp` and only reverted `modules/imgproc/src/accum.simd.hpp` to measure the baseline. Then I restored the SIMD patch and measured the optimized version.
Baseline measurement:
```sh
git diff -- modules/imgproc/src/accum.simd.hpp > accum_cn4_simd.patch
git checkout -- modules/imgproc/src/accum.simd.hpp
mkdir perf_logs
cmake -S . -B build_perf_before -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign > perf_logs\before_cmake_config.txt 2>&1
cmake --build build_perf_before --target opencv_perf_imgproc -j 4 > perf_logs\before_build.txt 2>&1
build_perf_before\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\before_perf.txt 2>&1
```
```sh
git diff -- modules/imgproc/src/accum.simd.hpp > accum_cn4_u8_simd.patch
git checkout -- modules/imgproc/src/accum.simd.hpp
cmake -S . -B build_perf_before_u8 -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_perf_before_u8 --target opencv_perf_imgproc -j 4
build_perf_before_u8\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask8UC4To32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\before_perf1.txt 2>&1
```
Optimized measurement:
```sh
git apply accum_cn4_simd.patch
cmake -S . -B build_perf_after -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign > perf_logs\after_cmake_config.txt 2>&1
cmake --build build_perf_after --target opencv_perf_imgproc -j 4 > perf_logs\after_build.txt 2>&1
build_perf_after\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask32FC4* --perf_min_samples=500 --perf_force_samples=500 > perf_logs\after_perf.txt 2>&1
```
```sh
git apply accum_cn4_u8_simd.patch
cmake -S . -B build_perf_after_u8 -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_perf_after_u8 --target opencv_perf_imgproc -j 4
build_perf_after_u8\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask8UC4To32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\after_perf1.txt 2>&1
```
Performance comparison was measured by keeping the new perf tests and only reverting modules/imgproc/src/accum.simd.hpp for the baseline run.
`-mstackrealign` was used for the MinGW Windows build to avoid stack-alignment issues with AVX code generation during local testing.
### Notes
The implementation keeps the existing scalar fallback path unchanged. SIMD is used only for the newly covered masked `cn == 4` vectorizable part, and any remaining tail elements are still handled by `acc_general_()`.
The improvement is most visible on larger images. Small images are dominated by overhead and do not always show meaningful speedup.
imgproc: add kernel size validation in boxFilter #29040
Check that ksize dimensions are strictly positive in boxFilter(), returning StsBadSize error instead of producing undefined behavior.
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imgproc: add findTRUContours - lock-free parallel contour extraction #28773
Integrates the TRUCO algorithm (Threaded Raster Unrestricted Contour Ownership, @cite TRUCO2026) as a transparent fast path inside `cv::findContours`. No API change is required: existing code automatically benefits when `mode=RETR_LIST` and no hierarchy output is requested.
### How it works
When `mode=RETR_LIST` and the caller does not request hierarchy, `findContours` delegates to the TRUCO parallel engine instead of Suzuki-Abe. All ContourApproximationModes are supported; approximation is applied in parallel after extraction. In all other cases the original Suzuki-Abe path is used unchanged.
### Key design ideas
- Row-strip domain decomposition parallelised via `cv::parallel_for_`
- Start-point ownership rule + speculative downward tracing eliminate tile stitching and synchronisation primitives (lock-free)
- 8-bit state space instead of the 32-bit integer labeling required by Suzuki-Abe, giving higher SIMD throughput and lower memory-bandwidth pressure
- Paged contour buffer (`TRUCOPagedContour`) avoids heap reallocation on the hot tracing path
- SIMD-accelerated row scanning via `cv::v_uint8` intrinsics
- Thread count controlled globally via `cv::setNumThreads()`, consistent with OpenCV conventions
### Output correctness
Significant effort has gone into ensuring the output is identical to the original `findContours` in every respect: same contour set, same ordering, and same approximation results for all methods. A dedicated test suite (`test_contours_truco.cpp`) verifies exact match against Suzuki-Abe across all four `ContourApproximationModes` and thread counts from 1 to 39, using noise images, circles, nested rectangles, and mixed scenes.
### Performance vs. Suzuki-Abe (from submitted paper)
- single-thread: ~1.8–1.9× faster (8-bit SIMD advantage)
- 20 threads: ~14–20× faster on i7-13700H (6P+8E, 20T)
- 20 threads: ~10–12× faster on Xeon Silver 4510 (12C/24T)
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fix: Guard ndsrvp filter center copy when source span is empty #28915
## Summary
This adds a defensive guard around the centre-region `memcpy` in the ndsrvp HAL filter padding path.
**Description**: Multiple memcpy calls in the ndsrvp HAL filter, bilateral filter, and median blur routines use computed sizes (e.g., cnes * (rborder - j), cn * (rborder - j), src_step * height) without validating that the computed byte count fits within the destination buffer. An attacker supplying a crafted image with manipulated dimensions, channel counts, or border parameters can cause the computed copy size to exceed the allocated destination buffer, resulting in a heap buffer overflow that corrupts adjacent memory.
## Changes
- `hal/ndsrvp/src/filter.cpp`
## Verification
- [x] Build passes
- [x] Scanner re-scan confirms fix
- [x] LLM code review passed
---
*Automated security fix by [OrbisAI Security](https://orbisappsec.com)*
hal/riscv-rvv: implement laplacian #28887
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1353
An implementation of laplacian for riscv-rvv hal
Added benchmark in perf_laplacian.cpp
The performance is evaluated on K1 by running
./opencv_perf_imgproc --gtest_filter="Perf_Laplacian*"
Results are attached in the figure below.
CV_8U fast path supports ksize=3 with BORDER_CONSTANT/BORDER_REPLICATE; unsupported combinations fall back to default implementation. See performance results bellow.
### Pull Request Readiness Checklist
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imgproc: add SIMD support for distance transform function #28636
- This PR adds OpenCV SIMD intrinsics-based optimizations to the distance transform functions for improved performance.
- The optimized implementation uses vectorized operations to accelerate the forward and backward passes of distance computation.
- In x64 architecture, distance transform function benefit from IPP-based optimized implementations. However, on ARM64 platforms, the execution falls back to scalar implementation, which results in lower performance.
- After introducing these changes, the distance transform functions showed noticeable performance improvements on Windows-ARM64.
<img width="800" height="706" alt="image" src="https://github.com/user-attachments/assets/9786606a-9d92-489d-a5ea-d2e57453ef02" />
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
fitEllipseDirect: replace det(M) threshold with eigenvector quality check
- Add Ts ≈ 0 guard to avoid division by zero in Schur complement
- Move eigenNonSymmetric inside perturbation loop
- Validate eigenvector with 4ac-b² > 1e-6*||v||² to filter garbage from
complex eigenvalues
fitEllipseAMS: scale threshold by 1/n^5 to match det(M) magnitude
Add Imgproc_FitEllipseDirect_NearCircular regression test
imgproc: fix minEnclosingCircle O(n^3) worst case by adding Welzl shuffle #28548
Welzl's algorithm requires random permutation of input points to
achieve expected O(n) time. Without shuffling, sorted inputs such
as those produced by findContours() trigger O(n^3) worst case.
Fix: copy input to std::vector<PT> and apply cv::randShuffle()
before processing. Uses OpenCV's RNG so cv::setRNGSeed() ensures
reproducible behavior.
Original benchmark (5088 contour points, Release, AVX2):
findContours output: 3.93 ms → 0.033 ms (119x speedup)
Random points: 0.051 ms → 0.049 ms (no regression)
Perf test results (this PR, Release, AVX2):
| Input | N | Time |
|-------|---|------|
| Sequential circle points | 10000 | 0.03 ms |
| Sequential circle points | 5000 | 0.01 ms |
| Random points (CV_32F) | 100000 | 1.87 ms |
| Random points (CV_32S) | 100000 | 2.81 ms |
Fixes#28546
### Pull Request Readiness Checklist
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In fitEllipseDirect, `double det = fabs(cv::determinant(M))` applies
fabs() unnecessarily since the next line `if (fabs(det) > 1.0e-10)`
already takes the absolute value. Remove the outer fabs() to avoid
the redundant operation.
Fix IPP warpAffine #28562
The issue is that we compute the inverse transformation and pass that to the HAL, but tell IPP that we use the actual transformation.
https://github.com/opencv/opencv/blob/a269a489b94b84717691533a04f79d9a0e5f479a/modules/imgproc/src/imgwarp.cpp#L2399-L2412
I have added an additional test with `CV_16S` Mats to test the IPP implementation, because right now no other `warpAffine` test enters the IPP branch.
This fixes#28554.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
This commit addresses a compiler warning encountered when building with GCC 13 and C++20.
The compiler triggers a false-positive -Wstringop-overflow warning in `findKSides`.
By adding `sides.reserve(k)`, we explicitly inform the compiler about the required
memory allocation, which suppresses the warning and provides a minor optimization
by avoiding potential reallocations.
Vadim Pisarevsky
Matt Van Horn
Alexander Smorkalov
Alexander Smorkalov
Siyu Wang
Vincent Rabaud
熊阔豪
Maksim Shabunin
Tiansuanyu
Rafael Muñoz Salinas
orbisai0security
Alex Wu
Ismail
Kumataro
kirilllapi
pratham-mcw
gideok Kim
Anshu
Murat Raimbekov
Adrian Kretz