Added OnnxRuntime GPU wrapper #28588
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GSoC 2025: Add Tokenizer Support to DNN Module #27534
merge with https://github.com/opencv/opencv_extra/pull/1276
### Summary
This pull request introduces initial support for a tokenizer module under `modules/dnn/src/tokenizer` as part of Google Summer of Code 2025 (Project: Tokenization for OpenCV DNN).
### Status
- [x] Project structure in place
- [x] Initial BPE tokenizer loading
- [x] Regex splitting (in progress)
- [x] Encoding logic for GPT-2 tokenizer (in progress)
- [ ] Documentation (to be improved)
### Goals
The goal is to support Hugging Face-compatible tokenization (e.g., GPT-2) natively in C++ to be integrated with DNN inference pipelines.
The core pipeline lives in `dnn/src/tokenizer/core_bpe.hpp` and `dnn/src/tokenizer/encoding.hpp`. For Unicode handling I’m using `dnn/src/tokenizer/unicode.hpp`, which is adapted from llama.cpp.
### Feedback
Please share early feedback on:
- General design structure
- Integration strategy with `dnn`
- Code organization or naming conventions
### Reference
Project: https://summerofcode.withgoogle.com/programs/2025/projects/79SW6eNK
core(opencl): fix inplace transpose race by enforcing LLSS ordering via local barrier #28686
The former inplace transpose implementation allowed a reordering of global-memory operations across work-items. Specifically, the intended LLSS (Load–Load–Store–Store) access pattern could be reordered by the GPU into LSLS (Load–Store–Load–Store), causing partially written tiles to be observed by other work-items and producing incorrect output.
This patch introduces a tiled LDS-based algorithm and adds an explicit:
barrier(CLK_LOCAL_MEM_FENCE);
between the load and store phases.
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python: fix segfault on 0-channel numpy array input #28747
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N/A: a Python unit test is added in `modules/python/test/test_mat.py`. No external test data required.
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N/A: this is a bug fix, not a new feature. No documentation update needed.
### Problem
Passing a numpy array with shape `(H, W, 0)` (0 channels) to any OpenCV function that accepts a `Mat` argument (e.g. `cv2.resize`, `cv2.warpAffine`, `cv2.blur`) causes a **segfault**.
**Reproducer:**
```python
import cv2
import numpy as np
arr = np.zeros((100, 100, 0), np.uint8)
cv2.resize(arr, (200, 200)) # segfault
```
### Root Cause
In `modules/python/src2/cv2_convert.cpp`, the numpy→Mat conversion checks channel validity only against `CV_CN_MAX` (upper bound):
```cpp
if (channels > CV_CN_MAX) // channels=0 passes this check
```
With `channels=0`, `CV_MAKETYPE(0, 0)` produces `type=-8`, which corrupts the Mat's internal type field and causes undefined behavior downstream.
### Fix
Extend the check to also reject `channels < 1`:
```cpp
if (channels < 1 || channels > CV_CN_MAX)
```
**After fix:**
```
cv2.error: src unable to wrap channels, invalid count (0, must be in [1, 512])
```
### Notes
- This affects all functions that accept a `Mat` input, not just `cv2.resize`
- The same bug exists in the `5.x` branch
- A 0-channel array has no valid OpenCV Mat representation; rejecting it with a clear error is the correct behavior and poses no backward-compatibility risk (the previous behavior was a crash)
Nms empty detection fix#28749
Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1330
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videoio(gstreamer): fix timestamp drift and color negotiation on Apple #28535
This commit addresses two issues on macOS with Apple M3 hardware:
1. Replaces floating-point timestamp math with gst_util_uint64_scale_int to ensure nanosecond precision.
2. Explicitly forces I420 format in the encoding profile to prevent hardware encoder negotiation failure.
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core: add NEON implementation for rotate function #28609
- This PR adds a NEON intrinsics-based implementation for the rotate function in matrix_transform.cpp for Windows-ARM64.
- The optimized implementation uses ARM NEON intrinsics to accelerate the internal transpose step used by the rotate function.
- In the x64 architecture, the rotate operation benefits from IPP-based optimized implementations. However, on ARM64, the execution falls back to the scalar implementation, which results in lower performance.
- To achieve performance parity with x64, a NEON-based SIMD implementation has been added for ARM64.
- After introducing these changes, the rotate function showed noticeable performance improvements on ARM64 platforms.
<img width="1009" height="817" alt="image" src="https://github.com/user-attachments/assets/8bec0041-b19c-4fc8-9103-532746224515" />
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dnn: fix missing ONNX dispatch entries and RotaryEmbedding bugs #28714
- Wire RMSNormalization and RotaryEmbedding parsers into the ONNX dispatch map. Both parse functions were declared and implemented but never registered, causing the importer to fall through to the generic path and skip constant-folding of cos/sin caches.
- Fix NonMaxSuppression dispatch key typo ("NonMaxSuprression" -> "NonMaxSuppression") so the ONNX op name matches the registered layer class. Also fix the type string set inside the parser.
- Fix tautological self-comparison in RotaryEmbeddingLayer::getMemoryShapes (cos_cache_shape.dims == cos_cache_shape.dims -> sin_cache_shape.dims).
- Fix typo in RotaryEmbedding error message ("cos_cahe" -> "cos_cache").
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dnn: fix Resize initNgraph for two-input case #28724
## Summary
Fixes the issue #28707
When a Resize/Upsample layer has two inputs, the data tensor and a reference tensor whose **shape** defines the output spatial size, the OpenVINO/NGRAPH backend's `initNgraph()` was ignoring `nodes[1]` entirely and relying solely on the `outHeight`/`outWidth` member variables.
These variables are set by `finalize()` from the pre-computed output blob dimensions. However, when the output shape is determined dynamically at runtime from the second input, `finalize()` sets them from the live tensor, but the OpenVINO backend calls `initNgraph()` to build a static compiled graph. If the member variables are 0 at that point, the compiled `Interpolate` node gets hardcoded with `{0, 0}` output dimensions, causing CV_Assert failure: {N,C,0,0} vs {N,C,H2,W2}
Fixes#28396 : out-of-bounds read in SIMD type conversion #28397Fixes#28396Fixes#27080
The vx_load_expand function in WASM intrinsics was using
wasm_v128_load which always loads a full 128-bit register
(16 bytes), even when the function only needed 8 elements.
For example, when converting uint8 to float32:
- vx_load_expand needs 8 uint8 elements
- But wasm_v128_load reads 16 bytes from memory
- This causes an 8-byte out-of-bounds read
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3d: fix depth precision inconsistency under ASan/RelWithDebInfo builds #28687
Resolves FMA inconsistency between build types by using std::fma(a, b, c) to get the same precision everywhere ASan, Release, Debug.
Change optimisation :
Without FMA : 96.54 ms
FMA : 32.33 ms
Merged with : opencv/ci-gha-workflow/pull/297
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Added conv 1x1 and conv 3x3 block layout support#28691
After this patch we get the following speed ups on resnet50.onnx:
Inference time after: ~7.6ms (inference time using onnxruntime: ~6.67ms)
Inference time before: ~14ms
Speed up: ~46% or 1.84x
Device details:
Model name: Intel(R) Core(TM) i9-14900KS
Cores: 32
RAM: 128 GB
Architecture: x86
OS: Ubuntu 24.04
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Added Output Tensor Names support in new DNN engine #28637
closes: https://github.com/opencv/opencv/issues/26201
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Optimize calibrateCamera with Schur‑complement LM and parallel Jacobian accumulation #28461
## Summary
- Optimized `calibrateCamera` for faster runtime without changing outputs using Schur‑complement LM, Parallel Jacobian accumulation, alongside other optimizations.
- Reduced time complexity from O(n^3) to O(n)
- Add a perf test that uses a 500-image chessboard dataset for performance testing.
## Performance
<img width="1200" height="800" alt="base_vs_fast_results" src="https://github.com/user-attachments/assets/6dafa19f-f9cb-4f7f-ba40-0940373712e8" />
<img width="1200" height="800" alt="fast_vs_ceres_results" src="https://github.com/user-attachments/assets/7157af27-8a2b-4810-8b53-3cc9972a8493" />
<img width="1200" height="800" alt="base_vs_fast_param_deviation" src="https://github.com/user-attachments/assets/fe4f954c-34f9-4b9a-b1b2-46e4c76ce08c" />
[Testing repo
](https://github.com/Ron12777/OpenCV-benchmarking)
## Testing
- All local tests pass
## Related
- [opencv_extra PR with test images](https://github.com/opencv/opencv_extra/pull/1312)
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Fix kernel shape handling in ONNX importer2 #28646
Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1323
closes: https://github.com/opencv/opencv/issues/28321
### Pull Request Readiness Checklist
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author vrooomy <vj.bro.833@gmail.com> 1773655621 +0530
committer vrooomy <vj.bro.833@gmail.com> 1774357668 +0530
added SIMD support for 64 bit float and fallback for 64 bit int
removed trailing white-spaces
add SIMD optimization for 32 unsinged int and clean fallback for 64u and 64s
add SIMD support for 32u and fallback for 64u and 64s
changed CalibrateDebevec test threshold to 0.25 (same as ARM) for IPP=NO
relaxed threshold to 0.22
conflict resolved
Convert exportResource() and loadResource() to use try-with-resources
to ensure InputStream, FileOutputStream, and ByteArrayOutputStream are
properly closed even when exceptions occur.
Also remove printStackTrace() in exportResource(), as the exception is
already rethrown as CvException with the original exception details.
Signed-off-by: ffccites <99155080+PDGGK@users.noreply.github.com>
Replace System.exit(-1) with exceptions in HighGui.java (#28696) #28699
## Summary
Library code should never call System.exit() as it kills the entire JVM. Replaced all 3 instances with appropriate exceptions.
Closes#28696
## Changes
- `imshow()` with empty image: `System.exit(-1)` -> `throw new IllegalArgumentException("Image is empty")`
- `waitKey()` with no windows: `System.exit(-1)` -> `throw new IllegalStateException("No windows created. Call imshow() first")`
- `waitKey()` with null window image: `System.exit(-1)` -> `throw new IllegalStateException("No image set for window: ... Call imshow() first")`
- `InterruptedException` catch: `printStackTrace()` -> `Thread.currentThread().interrupt()`
dnn: fix dst_dp assertion in broadcast for size-1 dims causing crash in lightglue.onnx model #28692
The original assertion CV_Assert(dst_dp == 1) does not handle valid cases where the innermost dimension size is 1 like [10, 5, 1], resulting in dst_dp == 0.
This occurs during broadcasting in LightGlue ONNX model and leads to assertion failure.
Allow dst_dp == 0 for size-1 dimensions to handle this edge case correctly.
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Added SIMD support for 64 bit float and fallback for 64 bit int#28663
Merged with : https://github.com/opencv/ci-gha-workflow/pull/299
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Alexander Smorkalov
Abhishek Gola
Jorge Velez
Alexander Smorkalov
Lurie97
LHOOL1109
vrooomy
Vincent Rabaud
Prasad Ayush Kumar
Ahmad
pratham-mcw
Shiyu Xie
SamareshSingh
Anshu
Varun Jaiswal
Rohan Mistry
Adrian Kretz
ffccites
Yang Guanyuhan
Weixie Cui