Fixed FP16 mat comparison in tests #26400
make sure that if both compared FP16/BF16 values are bitwise-equal, assume their difference to be 0 (zero), just like in the case of FP32 and FP64, don't try to compare them as floating-point numbers, because they can be NaN's.
**fixes** #24894
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Added extra tests for reshape #26254
Attempt to reproduce problems described in #25174. No success; everything works as expected. Probably, the function has been used improperly. Slightly modified the code of Mat::reshape() to provide better diagnostic.
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Add interface to Annoy which will replace the FLANN #25708
This PR is to add interface to [Annoy](https://github.com/spotify/annoy) which will replace the FLANN, part of one of the cleanup work of OpenCV 5.0: #24998.
After it, there will be consecutive patches:
- [ ] Add Annoy based DescriptorMatcher
- [ ] Replace FLANN based code with Annoy and remove FLANN completely
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Modified Caffe parser to support the new dnn engine #26208
Now the Caffe parser supports both the old and the new engine. It can be selected using newEngine argument in PopulateNet.
All cpu Caffe tests work fine except:
- Test_Caffe_nets.Colorization
- Test_Caffe_layers.FasterRCNN_Proposal
Both these tests doesn't work because of the bug in the new net.forward function. The function takes the name of the desired target last layer, but uses this name as the name of the desired output tensor.
Also Colorization test contains a strange model with a Silence layer in the end, so it doesn't have outputs. The old parser just ignored it. I think, the proper solution is to run this model until the (number_of_layers - 2) layer using proper net.forward arguments in the test.
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doc: fix doxygen errors at Algorithm and QRCodeEncoder #26373
Close https://github.com/opencv/opencv/issues/26372
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Fix hfloat conflicts of v_func in merging 4.x to 5.x #26369
This PR solves the conflicts in merging 4.x to 5.x https://github.com/opencv/opencv/pull/26358
1. Explicitly convert the inputs number for `v_setall_` to hfloat number
2. Loosens the threshold for `v_sincos` test. (related issue: https://github.com/opencv/opencv/issues/26362)
3. Remove the new but temp api `template <> inline v_float16x8 v_setall_(float v) { return v_setall_f16((hfloat)v); }`
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Use LMUL=2 in the RISC-V Vector (RVV) backend of Universal Intrinsic. #26318
The modification of this patch involves the RVV backend of Universal Intrinsic, replacing `LMUL=1` with `LMUL=2`.
Now each Universal Intrinsic type actually corresponds to two RVV vector registers, and each Intrinsic function also operates two vector registers. Considering that algorithms written using Universal Intrinsic usually do not use the maximum number of registers, this can help the RVV backend utilize more register resources without modifying the algorithm implementation
This patch is generally beneficial in performance.
We compiled OpenCV with `Clang-19.1.1` and `GCC-14.2.0` , ran it on `CanMV-k230` and `Banana-Pi F3`. Then we have four scenarios on combinations of compilers and devices. In `opencv_perf_core`, there are 3363 cases, of which:
- 901 (26.8%) cases achieved more than `5%` performance improvement in all four scenarios, and the average speedup of these test cases (compared to scalar) increased from `3.35x` to `4.35x`
- 75 (2.2%) cases had more than `5%` performance loss in all four scenarios, indicating that these cases are better with `LMUL=1` instead of `LMUL=2`. This involves `Mat_Transform`, `hasNonZero`, `KMeans`, `meanStdDev`, `merge` and `norm2`. Among them, `Mat_Transform` only has performance degradation in a few cases (`8UC3`), and the actual execution time of `hasNonZero` is so short that it can be ignored. For `KMeans`, `meanStdDev`, `merge` and `norm2`, we should be able to use the HAL to optimize/restore their performance. (In fact, we have already done this for `merge` #26216 )
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Finally dropped convertFp16 function in favor of cv::Mat::convertTo() #26327
Partially address https://github.com/opencv/opencv/issues/24909
Related PR to contrib: https://github.com/opencv/opencv_contrib/pull/3812
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Modified TFLite parser for the new dnn engine #26330
The new dnn graph is creating just by defining input and output names of each layer.
Some TFLite layers has fused activation, which doesn't have layer name and input and output names. Also some layers require additional preprocessing layers (e.g. NHWC -> NCHW). All these layers should be added to the graph with some unique layer and input and output names.
I solve this problem by adding additionalPreLayer and additionalPostLayer layers.
If a layer has a fused activation, I add additionalPostLayer and change input and output names this way:
**original**: conv_relu(conv123, conv123_input, conv123_output)
**new**: conv(conv123, conv123_input, conv123_output_additional_post_layer) + relu(conv123_relu, conv1_output_additional_post_layer, conv123_output)
If a layer has additional preprocessing layer, I change input and output names this way:
**original**: permute_reshape(reshape345, reshape345_input, reshape345_output)
**new**: permute(reshape345_permute, reshape345_input, reshape345_input_additional_pre_layer) + reshape(reshape345, reshape345_input_additional_pre_layer, reshape345_output)
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Fix#26322: construction of another Mat header for empty matrix #26333
The PR fixes#26322
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2x more accurate float => bfloat conversion #26321
There is a magic trick to make float => bfloat conversion more accurate (_original reference needed, is it done this way in PyTorch?_). In simplified form it looks like:
```
uint16_t f2bf(float x) {
union {
unsigned u;
float f;
} u;
u.f = x;
// return (uint16_t)(u.u >> 16); <== the old method before this patch
return (uint16_t)((u.u + 0x8000) >> 16);
}
```
it works correctly for almost all valid floating-point values, positive, zero or negative, and even for some extreme cases, like `+/-inf`, `nan` etc. The addition of `0x8000` to integer representation of 32-bit float before retrieving the highest 16 bits reduces the rounding error by ~2x.
The slight problem with this improved method is that the numbers very close to or equal to `+/-FLT_MAX` are mistakenly converted to `+/-inf`, respectively.
This patch implements improved algorithm for `float => bfloat` conversion in scalar and vector form; it fixes the above-mentioned problem using some extra bit magic, i.e. 0x8000 is not added to very big (by absolute value) numbers:
```
// the actual implementation is more efficient,
// without conditions or floating-point operations, see the source code
return (uint16_t)(u.u + (fabsf(x) <= big_threshold ? 0x8000 : 0)) >> 16);
```
The corresponding test has been added as well and this is output from the test:
```
[----------] 1 test from Core_BFloat
[ RUN ] Core_BFloat.convert
maxerr0 = 0.00774842, mean0 = 0.00190643, stddev0 = 0.00186063
maxerr1 = 0.00389057, mean1 = 0.000952614, stddev1 = 0.000931268
[ OK ] Core_BFloat.convert (7 ms)
```
Here `maxerr0, mean0, stddev0` are for the original method and `maxerr1, mean1, stddev1` are for the new method. As you can see, there is a significant improvement in accuracy.
**Note:**
_Actually, on ~32,000,000 random FP32 numbers with uniformly distributed sign, exponent and mantissa the new method is always at least as accurate as the old one._
The test also checks all the corner cases, where we see no degradation either vs the original method.
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imgcodecs: implement imencodemulti() #26211Close#26207
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G-API: Introduce level optimization flag for ONNXRT backend #26293
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Use border value in ipp version of warp affine #26313
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New dnn engine #26056
This is the 1st PR with the new engine; CI is green and PR is ready to be merged, I think.
Merge together with https://github.com/opencv/opencv_contrib/pull/3794
---
**Known limitations:**
* [solved] OpenVINO is temporarily disabled, but is probably easy to restore (it's not a deal breaker to merge this PR, I guess)
* The new engine does not support any backends nor any targets except for the default CPU implementation. But it's possible to choose the old engine when loading a model, then all the functionality is available.
* [Caffe patch is here: #26208] The new engine only supports ONNX. When a model is constructed manually or is loaded from a file of different format (.tf, .tflite, .caffe, .darknet), the old engine is used.
* Even in the case of ONNX some layers are not supported by the new engine, such as all quantized layers (including DequantizeLinear, QuantizeLinear, QLinearConv etc.), LSTM, GRU, .... It's planned, of course, to have full support for ONNX by OpenCV 5.0 gold release. When a loaded model contains unsupported layers, we switch to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* Some layers , e.g. Expat, are only partially supported by the new engine. In the case of unsupported flavours it switches to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* 'Concat' graph optimization is disabled. The optimization eliminates Concat layer and instead makes the layers that generate tensors to be concatenated to write the outputs to the final destination. Of course, it's only possible when `axis=0` or `axis=N=1`. The optimization is not compatible with dynamic shapes since we need to know in advance where to store the tensors. Because some of the layer implementations have been modified to become more compatible with the new engine, the feature appears to be broken even when the old engine is used.
* Some `dnn::Net` API is not available with the new engine. Also, shape inference may return false if some of the output or intermediate tensors' shapes cannot be inferred without running the model. Probably this can be fixed by a dummy run of the model with zero inputs.
* Some overloads of `dnn::Net::getFLOPs()` and `dnn::Net::getMemoryConsumption()` are not exposed any longer in wrapper generators; but the most useful overloads are exposed (and checked by Java tests).
* [in progress] A few Einsum tests related to empty shapes have been disabled due to crashes in the tests and in Einsum implementations. The code and the tests need to be repaired.
* OpenCL implementation of Deconvolution is disabled. It's very bad and very slow anyway; need to be completely revised.
* Deconvolution3D test is now skipped, because it was only supported by CUDA and OpenVINO backends, both of which are not supported by the new engine.
* Some tests, such as FastNeuralStyle, checked that the in the case of CUDA backend there is no fallback to CPU. Currently all layers in the new engine are processed on CPU, so there are many fallbacks. The checks, therefore, have been temporarily disabled.
---
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Proposed solution for the issue 26297 #26298closes#26297
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1) numerals are now monospace, e.g. '1' has the same width as '0',
2) '0' is different from capital 'o',
3) new glyphs added
2. stb_truetype upgraded from 1.24 to 1.26 with some fixes in rendering.
imgproc: update warpAffine opencl kernel to be in sync with cpu one #26292
Relates https://github.com/opencv/opencv/pull/26242
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Features2d cleanup: Move several feature detectors and descriptors to opencv_contrib #25292
features2d cleanup: #24999
The PR moves KAZE, AKAZE, AgastFeatureDetector, BRISK and BOW to opencv_contrib/xfeatures2d.
Related PR: opencv/opencv_contrib#3709
In that function, the floats are cast to int to be compared to 0.
But a float can be -0 or +0, hence
define CHECK_NZ_FP(x) ((x)*2 != 0)
to remove the sign bit. Except that can trigger the sanitizer:
runtime error: signed integer overflow: -1082130432 * 2 cannot be represented in type 'int'
Doing everything in uint instead of int is properly defined by the
standard.
Add support for v_sin and v_cos (Sine and Cosine) #25892
This PR aims to implement `v_sincos(v_float16 x)`, `v_sincos(v_float32 x)` and `v_sincos(v_float64 x)`.
Merged after https://github.com/opencv/opencv/pull/25891 and https://github.com/opencv/opencv/pull/26023
**NOTE:**
Also, the patch changes already added `v_exp`, `v_log` and `v_erf` to pass parameters by reference instead of by value, to match API of other universal intrinsics.
TODO:
- [x] double and half float precision
- [x] tests for them
- [x] doc to explain the implementation
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- Implemented a new `create` method in `FaceRecognizerSF` to allow model and configuration loading from memory buffers (std::vector<uchar>), similar to the existing functionality in `FaceDetectorYN`.
- Updated `face_recognize.cpp` with a new constructor in `FaceRecognizerSFImpl` that supports buffer-based loading for both model weights and network configuration.
- Ensured compatibility with both file-based and buffer-based model loading by maintaining consistent backend and target settings across both constructors.
- This change improves flexibility, allowing FaceRecognizerSF to be instantiated from memory buffers, which is useful for dynamic model loading scenarios such as embedded systems or applications where models are loaded in-memory.
Update Documentation #26260
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Update intrin_wasm.hpp #25909
See https://github.com/microsoft/vcpkg/issues/33443 for some build context when using
```vcpkg install opencv4:wasm32-emscripten```
`__EMSCRIPTEN_major__`, `__EMSCRIPTEN_minor__` and `__EMSCRIPTEN_tiny__` in `emsdk` >= 3.1.4 are in a header, as opposed to command line.
We could potentially be more aggressive with how I'm checking this property; let me know if I should make the change.
It should also be suggested that `-msimd128` is auto-included in the associated portfile for opencv, but that's a separate issue. Someone let me know if I should also make that change as well.
Special thanks to https://github.com/youar for supporting this work; please inform if applying a copyright-header is appropriate attribution.
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core: C-API cleanup: RNG algorithms in core(4.x) #26259
- replace CV_RAND_UNI and NORMAL to cv::RNG::UNIFORM and cv::RNG::NORMAL.
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extended Norm tests to prove that cv::norm() already supports all the types.
cv::norm() already provides enough functionality; just extended tests to prove it. See #24887
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- Added function overload for the simple case
- Added CV_Bool type support for masks
- `parallel_for_` for intrinsics calibration for faster inference
- Homogenize parameters order with other calibrateXXX functions
imgproc: add optimized warpAffine kernels for 8U/16U/32F + C1/C3/C4 inputs #25984
Merge wtih https://github.com/opencv/opencv_extra/pull/1198.
Merge with https://github.com/opencv/opencv_contrib/pull/3787.
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* use 2 parms for now to identify the error
* Revert "use 2 parms for now to identify the error"
This reverts commit 86faf993a7.
* replace += with =
* add v_log ref
* refactor intrin_math code
* Add include guard to `intrin_math.hpp` to prevent multiple inclusions
* rename VX to V; make fp64 impl in neon be optional
* add v_setall, v_setzero for all backends; rewrite the intrin_math
* fix error on rvv_scalable
* let v_erf use v_exp_default_32f function
* 1. replaced 'v_setzero(VecType dummy)' with 'v_setzero_<VecType>()'
2. replaced 'v_setall(LaneType x, VecType dummy)' with 'v_setall_<VecType>(LaneType x)'
3. added tests for the new v_setzero_<> and v_setall_<>.
* gcc does not seem to like static_assert in functions even when they are not used
* trying to fix compile errors in Debug mode on Linux
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Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
Reworked multiview calibration interface #26221
- Use InputArray / OutputArray
- Use enum for camera type
- Sort parameters according guidelines
- Made more outputs optional
- Introduce flags and added tests for intrinsics and extrinsics guess.
### 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
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] 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
Mirror most recent changes from https://github.com/terzakig/sqpnp/pull/24
- rank revealing QR in nullspace computation
- sqrt-free Cholesky (i.e., L*D*Lt) in the SQP solution
- replaced divisions with multiplications by inverses
- simplified checks in computeRowAndNullspace()
- removed unnecessary negations
- broke some dependency chains with parentheses
- minor other changes
