fix int32 overflow in shape_utils::total() for large tensors #29093
Fixes https://github.com/opencv/opencv/issues/24914
### Problem
When running inference with a ConvTranspose (deconvolution) layer on large inputs
(e.g. 4864×4864 with 30 channels), the DNN module crashes with:
OpenCV net_impl.cpp: error:
(expected: 'total(ints[i]) > 0'), where
'total(ints[i])' is -1455947776
must be greater than
'0' is 0
The root cause is `shape_utils::total()` which returns `int` (32-bit signed).
`ENGINE_CLASSIC` catches this via `CV_CheckGT` and throws.
`ENGINE_NEW` was silently bypassing the check — the overflow in `total()` itself
was never addressed.
### Changes
**`modules/dnn/include/opencv2/dnn/shape_utils.hpp`** — root fix
- Changed return type of both `total()` overloads from `int` to `size_t`
- Changed accumulator from `int elems = 1` to `size_t elems = 1`
**`modules/dnn/src/net_impl.cpp`**
- Updated `CV_CheckGT(total(...), 0)` to `CV_CheckGT(total(...), (size_t)0)`
to match the new return type
**`modules/dnn/src/net_impl2.cpp`**
- Added the same `CV_CheckGT` shape validation that `ENGINE_CLASSIC` has in
`net_impl.cpp:1333-1337` — `ENGINE_NEW` was missing this check entirely
**`modules/dnn/src/legacy_backend.hpp`**
- Removed the now-incorrect `(int)` cast in `CV_CheckEQ` — both sides are
now `size_t`
### Test
Added `Net.ShapeUtils_total_no_int32_overflow` in `modules/dnn/test/test_misc.cpp`:
- The shape [1920 × 1,478,656] is the exact im2col buffer from the bug report.
EXPECT_EQ verifies total() returns the correct size_t value 2,839,019,520.
EXPECT_LT documents that casting it to int wraps to -1,455,947,776 — the
value that caused the original crash.
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requires (YOLO26n , RetinaFace model download) : https://github.com/opencv/opencv_extra/pull/1357
added performance tests for 14 modern deep learning models to `modules/dnn/perf/perf_net.cpp.`
**Tests added**
- RT_DETR_L
- RF_DETR
- Grounding_DINO
- BlazeFace
- RetinaFace
- YOLO26n
- YOLO26m_Seg
- SegFormer_B2_Clothes
- Depth_Anything_V2
- SigLIP
- OWLv2
- RAFT
- SAM2_Encoder
- SAM2_Decoder
Models are available at: https://drive.google.com/drive/folders/1lpYAWSLMtxcmfDtYjiw0qwzZX0bCH-i4
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Add missing DataLayout constants in generated bindings for 5.x #29074
- Added DATA_LAYOUT_* constants to missing_consts so they
are manually injected into Core.java (same approach used for CV_8U, FILLED etc.)
- Added DataLayout entry to type_dict so the generator correctly maps
DataLayout
### Tests
modules/dnn/misc/java/test/DnnBlobFromImageWithParamsTest.java:
New test added:
- testDataLayoutConstants: verifies all DATA_LAYOUT_* constants are accessible from Core
Pre-existing tests enabled (were commented out earlier):
- testBlobFromImageWithParamsNHWCScalarScale: verifies blobFromImageWithParams
produces correct output with DATA_LAYOUT_NHWC and per-channel scalar scaling
- testBlobFromImageWithParams4chMultiImage: verifies blobFromImagesWithParams
correctly handles a batch of images with DATA_LAYOUT_NHWC layout
Closes : #27264
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[Bug Fix] Slice2 empty-range crash in new DNN engine#29029
Closes: https://github.com/opencv/opencv/issues/29023
Merge with: https://github.com/opencv/opencv_extra/pull/1363
## Summary
ENGINE_NEW asserted `CV_Assert(outsz >= 0)` in `slice2_layer.cpp` when an ONNX Slice op had `start > end` with positive step. Per ONNX spec, such slices are valid and
produce an empty-range output (size 0). The hard assertion crashed SwinIR inference on ENGINE_NEW.
## Fix
- Replaced the fatal assertion with a graceful clamp: when `outsz < 0`, set `outsz = 0` and `end = start`.
- Moved `allEnds[axis] = end` to after the clamp so downstream shape inference receives a consistent value (the previous order propagated un-clamped garbage end values
into `normalize_axis()`, causing SIGSEGV).
## Test
Adds `Reproducibility_SwinIR_ONNX` accuracy test in `test_model.cpp`. Test data registered in opencv_extra PR with matching branch name `fix/dnn-slice2-empty-range`.
- Without fix: 3/3 FAIL with `slice2_layer.cpp:124: (-215:Assertion failed) outsz >= 0`
- With fix: 3/3 PASS (CPU, OCL, OCL_FP16)
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SIMD Kernel speedup for DNN layers #28889
This PR add following speedups for Grounding Dino tiny model.
For Device: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW (Before)` | `ENGINE_NEW (After)` | `ENGINE_ORT` |
| :--- | :--- | :--- | :--- |
| **Grounding Dino Tiny** | 3130 ms | 1872.06 ms| 1800.18 ms|
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Fixed gatherND offset calculation #29084
Bug fix: fixed segmentation fault when running ALIKED onnx model with ENGINE_NEW.
### Description
issue: when running ALIKED onnx model with ENGINE_NEW, segmentation fault happens during the inference.
fix: fixed the offset calculation in gatherND.cpp, so gather does not access out-of-bound memory.
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Port of the 4.x patch to 5.x. Registers a new
ConsecutiveTransposePairsSubgraph in onnx_graph_simplifier.cpp's
simplifySubgraphs() so that consecutive Transpose nodes whose composed
permutation is identity are eliminated at the ONNX proto level. This
runs before either engine takes over, so both ENGINE_CLASSIC and
ENGINE_NEW benefit.
Equivalent optimizations exist in tf2onnx (TransposeOptimizer._transpose_handler)
and ONNX Runtime (HandleTransposeImpl, 'Permutations cancel' branch).
core: add YAML 1.2 support for FileStorage #28482Fixes: #26363
CI Update: https://github.com/opencv/ci-gha-workflow/pull/306
Summary:
- Bool true/false literals support
- Header-less YAMLs support for Python YAML module compatibility. Header-less files are parsed as YAMLs by default
- Added FORMAT_YAML_1_0 flag to FileStorage for fallback.
- New YAML1.2 header
Testing:
- Added Core_InputOutput.YAML_1_2_Compatibility test case in test_io.cpp.
- Added YAML interop test in Python
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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Fixed subgraph name scoping in new DNN engine #28971
closes: https://github.com/opencv/opencv/issues/23663, https://github.com/opencv/opencv/issues/19977
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1362
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RISC-V: add RVV convertScale paths for selected depth conversions #29058
### Summary
This PR extends the RISC-V RVV HAL `convertScale` implementation with optimized paths for selected `convertTo()` depth conversions:
- `CV_32F -> CV_8U`
- `CV_16U -> CV_32F`
- `CV_16S -> CV_32F`
The change is confined to `hal/riscv-rvv/src/core/convert_scale.cpp`. It does not modify the generic `convertTo()` implementation or affect dispatch for non-RISC-V targets.
### Rationale
`Mat::convertTo()` reaches architecture-specific implementations through the HAL `convertScale` interface. Adding these paths in the RVV HAL keeps the optimization in the existing backend layer and avoids introducing RISC-V-specific branches into common OpenCV code.
The selected conversions were chosen based on measured benefit and practical relevance. `CV_32F -> CV_8U` provides the largest speedup, while `CV_16U -> CV_32F` and `CV_16S -> CV_32F` provide consistent improvements for common image-processing data paths.
### Benchmark
Tested on Banana Pi BPI-F3, an 8-core RISC-V board with RVV 1.0 support.
Build and test configuration:
- OpenCV `4.14.0-pre`
- Release build
- RVV HAL enabled
- Compiler: `riscv64-unknown-linux-gnu-g++ 14.2.1`
- Test binary: `opencv_perf_core`
- Test filter: `*convertTo*`
- Samples: `--perf_force_samples=20 --perf_min_samples=20`
Benchmark command:
```bash
./opencv_perf_core \
--gtest_filter=*convertTo* \
--perf_force_samples=20 \
--perf_min_samples=20
```
Each benchmark case was measured with 20 forced samples. The baseline/after comparison was repeated to check run-to-run stability. The optimized conversion pairs showed consistent improvements.
Second-run comparison for the optimized pairs:
```text
OPTIMIZED_PAIRS: 24 cases, gmean 2.6009x
CV_32F -> CV_8U: 16.0718x
CV_16U -> CV_32F: 1.2334x
CV_16S -> CV_32F: 1.1376x
```
`CV_32F -> CV_8U` showed the largest improvement, with speedups in the `14.5x` to `18.8x` range across the tested image sizes, channel counts, and alpha values.
### Notes on Measurement
The initial benchmark run showed noise in conversion pairs not touched by this PR, likely due to board-level variance such as frequency scaling, thermal state, or background scheduling.
A second run showed untouched pairs close to neutral:
```text
UNTOUCHED_OTHER: gmean 0.9941x
```
The optimized conversion pairs remained consistently faster across both runs, with no observed regression in the added RVV paths.
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Rvv core norm #29057Fixesopencv/opencv#29052
### Problem
`Core_Norm/ElemWiseTest.accuracy/0` failed on RISC-V with RVV enabled when computing norm for `CV_16S` data.
The reported failing case was:
```text
src[0] ~ 16sC4 3-dim (1 x 116 x 40)
```
The expected norm result was a large positive `double`, but the RVV path returned an incorrect value:
```text
expected: 3370900308417
actual: -173296
```
This indicates that the problem was not in the public `cv::norm()` API, but in the RVV HAL implementation used for the `CV_16S` L2/L2SQR accumulation path.
### Root Cause
The RVV HAL has a specialized implementation for `CV_16S` L2 norm:
```cpp
NormL2_RVV<short, double>
```
The implementation widens `int16` values, squares them, converts the widened products to `float64`, accumulates them in an `f64m8` vector, and finally reduces the vector to a scalar `double`:
```cpp
auto s = __riscv_vfmv_v_f_f64m8(0, vlmax);
...
auto v_mul = __riscv_vwmul(v, v, vl);
s = __riscv_vfadd_tu(s, s, __riscv_vfwcvt_f(v_mul, vl), vl);
...
return __riscv_vfmv_f(__riscv_vfredosum(...));
```
The bug was in the scalar initializer passed to `__riscv_vfredosum`.
Before this patch, the code created an `f64m1` scalar vector but used the maximum vector length for `e32m1`:
```cpp
__riscv_vfmv_s_f_f64m1(0, __riscv_vsetvlmax_e32m1())
```
This is inconsistent: the vector type is `f64m1`, so the VL used to initialize it must correspond to `e64m1`, not `e32m1`.
objdetect(aruco): fix maxCorrectionBits in predefined dictionaries #29034
This PR fixes the bugs in the creation of some dictionaries and standarizes its construction.
## Bugs
1. **DICT_ARUCO_MIP_36h12_DATA** was incorrectly assigned `maxCorrectionBits=12`, which is the intermarker distance. This was causing a high rate of false positives during detection, which is a significant problem.
2. **APRILTAG** dictionaries were incorrectly created with `maxCorrectionBits=0`, making it impossible to do error correction.
## Solution
Given a intermarker distance X, the correct `maxCorrectionBits = X/2 - 1`. Thus, standardize the construction of Dictionaries as such.
libpng upgrade to 1.6.57 #29051
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Python: add context manager compatibility for VideoCapture and VideoWriter #28967
### Description
This PR implements the context manager protocol (`with` statement support) for `cv2.VideoCapture` and `cv2.VideoWriter` objects in Python.
### Problem
Currently, these classes do not support the context manager protocol, leading to a `TypeError: ... object does not support the context manager protocol` when used with a `with` block. As reported in issue #28956, if an exception occurs before an explicit `.release()` call, the video resource or file remains locked in the system[cite: 298, 309].
### Identification & Motivation
By analyzing the issue and the Python binding structure of OpenCV, I identified that while the core logic resides in C++, the Pythonic behavior can be enhanced by injecting `__enter__` and `__exit__` methods during the module initialization. This ensures that resources are automatically and safely released even if the program execution is interrupted by an error[cite: 298, 325].
### Solution
I have modified `modules/python/package/cv2/__init__.py` to dynamically inject the necessary methods:
1. Defined `_video_enter` to return the object itself (standard context manager behavior).
2. Defined `_video_exit` to trigger the `.release()` method automatically.
3. Injected these methods into `VideoCapture` and `VideoWriter` classes immediately after the `bootstrap()` function to ensure the native C++ classes are correctly loaded into the global namespace.
Fixes#28956
## Summary
Replace the existing RVV HAL FAST-16 implementation (`hal/riscv-rvv/src/features2d/fast.cpp`) with a score-first approach, achieving **2-4x speedup** over the previous HAL implementation on RISC-V hardware.
## Approach
The previous HAL implementation used a count-based approach (XOR trick + per-pixel bitmask scanning + separate cornerScore computation). This PR replaces it with a **score-first** approach that processes multiple horizontal pixels in parallel using RVV vectors.
Key optimizations:
1. **Score-first approach**: Directly compute corner scores via min/max reduction over all 9-arc windows, then threshold on score. This eliminates the arc-counting pass and the separate `cornerScore` function.
2. **u8 to i16 zero-extension**: Zero-extend pixel values to int16 for natural signed comparison, eliminating the XOR-delta trick and preventing score overflow.
3. **vcompress batch output**: Extract all corner indices in one vector operation instead of per-lane bitmask scanning.
4. **vlen-adaptive**: Uses `vsetvl_e16m1` for dynamic vector length — works on any RVV 1.0 implementation regardless of VLEN (128, 256, 512, etc.) without code changes.
## Performance
Benchmarked by maintainer on **Muse Pi v3.0** (GCC, SpacemiT toolchain v1.0.4):
| Test | Previous HAL (ms) | This PR (ms) | Speedup |
|------|-------------------|--------------|---------|
| FAST-20 NMS=true | 23.06 | 7.99 | **2.89x** |
| FAST-20 NMS=true (orig) | 7.31 | 1.80 | **4.06x** |
| FAST-30 NMS=false | 20.10 | 7.75 | **2.59x** |
| FAST_DEFAULT s2.jpg | 76.94 | 19.06 | **4.04x** |
| ORB_DEFAULT chess9 | 64.46 | 25.43 | **2.53x** |
Both NMS=true and NMS=false cases are supported.
## Files Changed
- **`hal/riscv-rvv/src/features2d/fast.cpp`**: Replaced count-based implementation with score-first approach
- No changes to `modules/features2d/src/fast.cpp` or any other files
## Test Plan
- [x] Built with RISC-V cross-compilation toolchain (GCC 14.2, RVV 1.0)
- [x] Tested on QEMU (rv64, v=true, vlen=256) — correct results for both NMS modes
- [x] Maintainer tested on Muse Pi v3.0 hardware — 2-4x speedup confirmed
- [ ] CI passes on all platforms
Fix CMake crash when CUDA arch autodetection comes up empty #28954
With CUDA 13 and a system gcc that the toolkit does not support, every NVCC probe inside ocv_filter_available_architecture fails and the result list ends up empty. The next line then calls list(GET ... -1) on that empty list, so CMake dies with the unhelpful message about list GET given empty list and the user has no obvious next step. This change replaces that path with a fatal error that points at the three real fixes, namely setting CUDA_ARCH_BIN, setting CUDA_HOST_COMPILER, or enabling OPENCV_CMAKE_CUDA_DEBUG to see what NVCC actually printed. It also forwards CMAKE_CUDA_HOST_COMPILER into CUDA_HOST_COMPILER since the issue reporter passed the standard CMake variable and it was being silently ignored.
Fixes#28952
Added net profiling support #28752
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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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Use Xcode DocC tool for buiding Apple platform docs + add quick-start tutorial #28704
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This PR resolves https://github.com/opencv/opencv/issues/28702 and https://github.com/opencv/opencv/issues/28703
Additionally it resolves build issues on Apple platforms by making build scripts run with Python 3
Add ARMPL support for DFT Function #28664
- This PR introduces hal/armpl/ with implementation of 1D, 2D DFT and DCT routines using ARM Performance Libraries as a custom HAL replacement for OpenCV's DFT & DCT Function.
- ArmPL MSI package is automatically downloaded and extracted via CMake when building on Windows ARM64, with a WITH_ARMPL option that defaults to ON for that platform.
- Forward and inverse real DFT calls in dxt.cpp are routed through ArmPL when available, with scaling applied only when needed.
- Test error thresholds in test_dxt.cpp are relaxed (from 1e-5 to 2e-4 for float ) to account for numerical differences between ArmPL and OpenCV's reference DFT results.
**Performance Benchmarks :**
<img width="993" height="835" alt="image" src="https://github.com/user-attachments/assets/76def647-6d20-4bce-8bc9-7363e723669f" />
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- Implement inrange_simd() covering uchar/ushort/short, chan=1/2/3/4
- Replace legacy CV_SIMD128 run_inrange3 with modern CV_SIMD API
- Extend perf tests to cover all added type/channel combinations
- Resolves TODO introduced in #12608 (2018)
Varun Jaiswal
Alexander Smorkalov
Abhishek Gola
Naresh
Shelia J.
Alexander Smorkalov
Vadim Pisarevsky
Kevin Lin
Om Navin Rope
satyam yadav
熊阔豪
Teddy-Yangjiale
Rafael Muñoz Salinas
Maksim Shabunin
Michael Klatis
Ebrahim Namdari
Shengyu-Wu
SamareshSingh
Giles Payne
Pratham Kumar
manfred