dnn: refactor ONNX MatMul with fastGemm #24694
Done:
- [x] add backends
- [x] CUDA
- [x] OpenVINO
- [x] CANN
- [x] OpenCL
- [x] Vulkan
- [x] add perf tests
- [x] const B case
### Benchmark
Tests are done on M1. All data is in milliseconds (ms).
| Configuration | MatMul (Prepacked) | MatMul | InnerProduct |
| - | - | - | - |
| A=[12, 197, 197], B=[12, 197, 64], trans_a=0, trans_b=0 | **0.39** | 0.41 | 1.33 |
| A=[12, 197, 64], B=[12, 64, 197], trans_a=0, trans_b=0 | **0.42** | 0.42 | 1.17 |
| A=[12, 50, 64], B=[12, 64, 50], trans_a=0, trans_b=0 | **0.13** | 0.15 | 0.33 |
| A=[12, 50, 50], B=[12, 50, 64], trans_a=0, trans_b=0 | **0.11** | 0.13 | 0.22 |
| A=[16, 197, 197], B=[16, 197, 64], trans_a=0, trans_b=0 | **0.46** | 0.54 | 1.46 |
| A=[16, 197, 64], B=[16, 64, 197], trans_a=0, trans_b=0 | **0.46** | 0.95 | 1.74 |
| A=[16, 50, 64], B=[16, 64, 50], trans_a=0, trans_b=0 | **0.18** | 0.32 | 0.43 |
| A=[16, 50, 50], B=[16, 50, 64], trans_a=0, trans_b=0 | **0.15** | 0.25 | 0.25 |
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Patch to opencv_extra has the same branch name.
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Add support for Orbbec Gemini2 and Gemini2 XL camera #24666
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The opencv zlib patch is still not merged. So I applied it to zlib-1.3.
Since zlib-1.3 didn't modify CMakeLists.txt, opencv's zlib
CMakeLists.txt doesn't need to be modified.
Android Tutorial for Windows Updated #24700
This PR updates Android tutorials parts related to Windows
### Pull Request Readiness Checklist
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- [ ] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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Fix path to ONNX Runtime include folder #24601
### Pull Request Readiness Checklist
Looks the `install` directory layout has been changed for `v1.16.3`
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [ ] I agree to contribute to the project under Apache 2 License.
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- [ ] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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Make default axis of softmax in onnx "-1" without opset option #24613
Try to solve problem: https://github.com/opencv/opencv/pull/24476#discussion_r1404821158
**ONNX**
`opset <= 11` use 1
`else` use -1
**TensorFlow**
`TF version = 2.x` use -1
`else` use 1
**Darknet, Caffe, Torch**
use 1 by definition
G-API: Support CoreML Execution Providers for ONNXRT Backend #24068
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Patch to opencv_extra has the same branch name.
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G-API: Get input model layout from the IR if possible in OV 2.0 backend #24658
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Android sample for VideoWriter #24592
This PR:
* adds an Android sample for video recording with MediaNDK and built-in MJPEG.
* adds a flag `--no_media_ndk` for `build_sdk.py` script to disable MediaNDK linkage.
### Pull Request Readiness Checklist
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- [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.
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Android camera tutorial update #24692
This PR extends the OpenCV 4 Android tutorial by a simple camera app based on existing code.
This part was accidentally removed during the #24653 preparation, this PR restores it and aligns it to the latest Android Studio.
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Classify and extend convolution and depthwise performance tests #24547
This PR aims to:
1. Extend the test cases from models: `YOLOv5`, `YOLOv8`, `EfficientNet`, `YOLOX`, `YuNet`, `SFace`, `MPPalm`, `MPHand`, `MPPose`, `ViTTrack`, `PPOCRv3`, `CRNN`, `PPHumanSeg`. (371 new test cases are added)
2. Classify the existing convolution performance test to below cases
- CONV_1x1
- CONV_3x3_S1_D1 (winograd)
- CONV
- DEPTHWISE
3. Reduce unnecessary test cases by follow 3 rules (366 test cases are pruned):
(i). For all tests, except for pad and bias related parameters, all other parameters are the same. Only one case can be reserved.
(ii). When the only difference is the channel of input shape, and other parameters are the same. Only one case can be reserved in each range `[1, 3], [4, 7], [8, 15], [16, 31], [32, 63], [64, 127], [128, 255], [256, 511], [512, 1023], [1024, 2047], [2048, 4095]`
(iii). When the only difference is the width and height of input shape, and other parameters are the same. Only one case can be reserved in each range `[1, 31], [32, 63], [64, 95]... `
> **Reproduced**: 1. follow step in https://github.com/alalek/opencv/commit/dnn_dump_conv_kernels to dump all convolution cases from new models. (declared flops may not right, need to be checked manually) 2 and 3. Use the script from python code [classify conv.txt](https://github.com/opencv/opencv/files/13522228/classify.conv.txt)
**Performance test result on Apple M2**
**Test result details**: [M2.md](https://github.com/opencv/opencv/files/13379189/M2.md)
**Additional test result details with FP16**: [m2_results_with_fp16.zip](https://github.com/opencv/opencv/files/13491070/m2_results_with_fp16.zip)
**Brief summary for 4.8.1 vs 4.7.0 or 4.6.0**:
1. `CONV_1x1_S1_D1` dropped significant with small or large input shape.
2. `DEPTHWISE_5x5 ` dropped a little compared with 4.7.0.
---
**Performance test result on [Intel Core i7-12700K](https://www.intel.com/content/www/us/en/products/sku/134594/intel-core-i712700k-processor-25m-cache-up-to-5-00-ghz/specifications.html)**: 8 Performance-cores (3.60 GHz, turbo up to 4.90 GHz), 4 Efficient-cores (2.70 GHz, turbo up to 3.80 GHz), 20 threads.
**Test result details**: [INTEL.md](https://github.com/opencv/opencv/files/13374093/INTEL.md)
**Brief summary for 4.8.1 vs 4.5.5**:
1. `CONV_5x5_S1_D1` dropped significant.
2. `CONV_1x1_S1_D1`, `CONV_3x3_S1_D1`, `DEPTHWISE_3x3_S1_D1`, `DEPTHWISW_3x3_S2_D1` dropped with small input shape.
---
TODO:
- [x] Perform tests on arm with each opencv version
- [x] Perform tests on x86 with each opencv version
- [x] Split each test classification with single test config
- [x] test enable fp16
doc: add crosscompile_with_multiarch #24629
Add cross compile tutorial for ubuntu/debian.
( I'm sorry to my poor English. )
Fix https://github.com/opencv/opencv/issues/24628
### Pull Request Readiness Checklist
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Speed up ChessBoardDetector::findQuadNeighbors #24605
### Pull Request Readiness Checklist
Replaced brute-force algorithm with O(N^2) time complexity with kd-tree with something like O(N * log N) time complexity (maybe only in average case).
For example, on image from #23558 without quads filtering (by using `CALIB_CB_FILTER_QUADS` flag) finding chessboards corners took ~770 seconds on my laptop, of which finding quads neighbors took ~620 seconds.
Now finding chessboards corners takes ~155-160 seconds, of which finding quads neighbors takes only ~5-10 seconds.
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
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Patch to opencv_extra has the same branch name.
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Add test for YoloX Yolo v6 and Yolo v8 #24611
This PR adds test for YOLOv6 model (which was absent before)
The onnx weights for the test are located in this PR [ #1126](https://github.com/opencv/opencv_extra/pull/1126)
### Pull Request Readiness Checklist
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ht_dec.c: Improve MSVC arm64 popcount performance #24205
Use NEON instructions for ARM64 (implementation based on https://github.com/microsoft/STL/pull/2127, which is Apache licensed).
Godbolt output here: https://godbolt.org/z/q7GPTqT14
Related patch to openjpeg: https://github.com/uclouvain/openjpeg/pull/1479
### Pull Request Readiness Checklist
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Yuantao Feng
Alexander Smorkalov
zzuliys
MengqingCao
Letu Ren
Tomoaki Teshima
MaximSmolskiy
Alexander Alekhin
Kumataro
Rostislav Vasilikhin
Vincent Rabaud
Anatoliy Talamanov
Wanli
Kirin Chu
Alexey Smirnov
Rostislav Vasilikhin
AleksandrPanov
Abduragim Shtanchaev
Peter Johnson
Alexander Smorkalov
Dmitry Kurtaev