HAL mul8x8to16 added #25506Fixes#25034
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HAL for projectPoints() added #25511
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Feature barcode detector parameters #24903
Attempt to solve #24902 without changing the default detector behaviour.
Megre with extra: https://github.com/opencv/opencv_extra/pull/1150
**Introduces new parameters and methods to `cv::barcode::BarcodeDetector`**.
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imgproc: C-API cleanup, drawContours refactor #25564
Changes:
* moved several macros from types_c.h to cvdef.h (assuming we will continue using them)
* removed some cases of C-API usage in _imgproc_ module (`CV_TERMCRIT_*` and `CV_CMP_*`)
* refactored `drawContours` to use C++ API instead of calling `cvDrawContours` + test for filled contours with holes (case with non-filled contours is simpler and is covered in some other tests)
#### Note:
There is one case where old drawContours behavior doesn't match the new one - when `contourIdx == -1` (means "draw all contours") and `maxLevel == 0` (means draw only selected contours, but not what is inside).
From the docs:
> **contourIdx** Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
> **maxLevel** Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
Old behavior - only one first contour is drawn:
a
New behavior (also expected by the test) - all contours are drawn:
Current net exporter `dump` and `dumpToFile` exports the network structure (and its params) to a .dot file which works with `graphviz`. This is hard to use and not friendly to new user. What's worse, the produced picture is not looking pretty.
dnn: better net exporter that works with netron #25582
This PR introduces new exporter `dumpToPbtxt` and uses this new exporter by default with environment variable `OPENCV_DNN_NETWORK_DUMP`. It mimics the string output of a onnx model but modified with dnn-specific changes, see below for an example.
## Usage
Call `cv::dnn::Net::dumpToPbtxt`:
```cpp
TEST(DumpNet, dumpToPbtxt) {
std::string path = "/path/to/model.onnx";
auto net = readNet(path);
Mat input(std::vector<int>{1, 3, 640, 480}, CV_32F);
net.setInput(input);
net.dumpToPbtxt("yunet.pbtxt");
}
```
Set `export OPENCV_DNN_NETWORK_DUMP=1`
```cpp
TEST(DumpNet, env) {
std::string path = "/path/to/model.onnx";
auto net = readNet(path);
Mat input(std::vector<int>{1, 3, 640, 480}, CV_32F);
net.setInput(input);
net.forward();
}
```
---
Note:
- `pbtxt` is registered as one of the ONNX model suffix in netron. So you can see `module: ai.onnx` and such in the model.
- We can get the string output of an ONNX model with the following script
```python
import onnx
net = onnx.load("/path/to/model.onnx")
net_str = str(net)
file = open("/path/to/model.pbtxt", "w")
file.write(net_str)
file.close()
```
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Check range for type-dependant function tables #25598
Address https://github.com/opencv/opencv/issues/24703
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Disabled conversion to float of model's input #25555
In dnn 4.x usually any model's input is converted to float32 or float16 (except quantized models). Also mean and scale can be applied. In current dnn 5.x there is the same conversion except int32 and int64 types. I removed this conversion.
Here is how the pipeline works now:
- if input Mat type is float32, the pipeline applies mean and scale and may convert it to float16.
- if input Mat type is not float32, the pipeline preserves the input type and doesn't apply mean and scale
There was a conflict in protobuf parser between ONNX importer and tests. In ONNX importer any uint8 weight was handled as quantized weight and x = int8(x_uint8 - 128) conversion was used inside the protobuf parser. ONNX conformance tests used the same protobuf reader, so tests with uint8 inputs couldn't read the input values properly. I've made this conversion optional.
These ONNX conformance tests are enabled:
- test_add_uint8
- test_div_uint8
- test_mul_uint8
- test_sub_uint8
- test_max_int8
- test_max_uint8
- test_min_int8
- test_min_uint8
- test_mod_mixed_sign_int8
- test_mod_uint8
These tests were removed:
- Test_two_inputs.basic (when input is uint8)
- setInput.normalization (when input is uint8)
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Integrate ARM KleidiCV as OpenCV HAL #25443
The library source code with license: https://gitlab.arm.com/kleidi/kleidicv/
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Support Transpose op in TFlite #25297
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1168
The purpose of this PR is to introduce support for the Transpose op in TFlite format and to add a shape comparison between the output tensors and the references. In some occasional cases, the shape of the output tensor is `[1,4,1,1]`, while the shape of the reference tensor is `[1,4]`. Consequently, the norm check incorrectly reports that the test has passed, as the residual is zero.
Below is a Python script for generating testing data. The generated data can be integrated into the repo `opencv_extra`.
```python
import numpy as np
import tensorflow as tf
PREFIX_TFL = '/path/to/opencv_extra/testdata/dnn/tflite/'
def generator(input_tensor, model, saved_name):
# convert keras model to .tflite format
converter = tf.lite.TFLiteConverter.from_keras_model(model)
#converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.optimizations = [None]
tflite_model = converter.convert()
with open(f'{PREFIX_TFL}/{saved_name}.tflite', 'wb') as f:
f.write(tflite_model)
# save the input tensor to .npy
if input_tensor.ndim == 4:
opencv_tensor = np.transpose(input_tensor, (0,3,1,2))
else:
opencv_tensor = input_tensor
opencv_tensor = np.copy(opencv_tensor, order='C').astype(np.float32)
np.save(f'{PREFIX_TFL}/{saved_name}_inp.npy', opencv_tensor)
# generate output tenosr and save it to .npy
mat_out = model(input_tensor).numpy()
mat_out = np.copy(mat_out, order='C').astype(np.float32)
if mat_out.ndim == 4:
mat_out = np.transpose(mat_out, (0,3,1,2))
interpreter = tf.lite.Interpreter(model_content=tflite_model)
out_name = interpreter.get_output_details()[0]['name']
np.save(f'{PREFIX_TFL}/{saved_name}_out_{out_name}.npy', mat_out)
def build_transpose():
model_name = "keras_permute"
mat_in = np.array([[[1,2,3], [4,5,6]]], dtype=np.float32)
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(2,3)))
model.add(tf.keras.layers.Permute((2,1)))
model.summary()
generator(mat_in, model, model_name)
if __name__ == '__main__':
build_transpose()
```
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Remove dnn::layer::allocate in doc #25591
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Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
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highgui: wayland: expand image width if title bar cannot be shown
Close#25560
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Additional fixes to 0/1D tests #25487
This has additional fixes requited for 0/1D tests.
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1/0D test padding layer #25390
This PR introduces 0/1D test for `padding` layer.
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Improved segmentation sample #25559#25006
This pull request replaces caffe models with onnx for the dnn segmentation sample in cpp and python
fcnresnet-50 and fcnresnet-101 has been replaced
u2netp (foreground-background) segmentation onnx model has been added [U2NET](https://github.com/xuebinqin/U-2-Net)
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0/1D test for tile layer #25409
This PR introduces `0/1D` test for `Tile` layer. It also add fuctionality to support `0/1D` cases.
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Fixed OpenVINO gemm layer #25518
Fixed OpenVINO gemm layer
The problem was that our layer didn't properly handle all the possible gemm options in OpenVINO mode
Fixes#25472
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Currently, there is an warning when CMake >= 3.27,
CMake Warning (dev) at cmake/OpenCVUtils.cmake:144 (find_package):
Policy CMP0148 is not set: The FindPythonInterp and FindPythonLibs modules
are removed. Run "cmake --help-policy CMP0148" for policy details. Use
the cmake_policy command to set the policy and suppress this warning.
This patch sets policy 0148 explicitly to suppress the warning.
0/1D Einsum Layer Test #25567
This PR introduces 0/1D test cases for Einsum layer.
TODO:
- Add support for 0D tensors to Einsum layer
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Currently, zlib-ng version is 'zlib ver #define ZLIB_VERSION "1.3.0.zlib-ng"'. Because ocv_parse_header_version only accepts dot and numbers and doesn't accepts 1.3.0.zlib-ng. This patch changes ocv_parse_header_version to accept all characters between parentheses.
highgui: wayland: fix to pass highgui test #25551Close#25550
- optimize Mat to XRGB8888 conversion with OpenCV functions
- extend to support CV_8S/16U/16S/32F/64F
- extend to support 1/4 channels
- fix to update value timing
- initilize slider_ value if value is not nullptr.
- Update user-ptr value and call on_change() function if cv_wl_trackbar::draw() is not called.
- Update usage of WAYLAND/XDG macro to avoid reference undefined macro.
- Update documents
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Merge pull request #25565 from savuor/rv/hal_eq_hist
HAL for equalizeHist() added #25565fixes#25530
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Transform offset to indeces for MatND in minMaxIdx HAL #25563
Address comments in https://github.com/opencv/opencv/pull/25553
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Merge pull request #25554 from savuor:rv/hal_lut
HAL for LUT added #25554
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Change opencv_face_detector related tests and samples from caffe to onnx #25463
Part of https://github.com/opencv/opencv/issues/25314
This PR aims to change the tests related to opencv_face_detector from caffe framework to onnx. Tests in `test_int8_layer.cpp` and `test_caffe_importer.cpp` will be removed in https://github.com/opencv/opencv/pull/25323
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core: add universal intrinsics for fp16 #25196
Partially resolves the section "Universal intrinsics evolution in OpenCV 5.0" in https://github.com/opencv/opencv/issues/25019.
Universal intrinsics for bf16 will be added in a subsequent pull request.
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Fix HAL interface for hal_ni_minMaxIdx #25553
Fixes https://github.com/opencv/opencv/issues/25540
The original implementation call HAL with the same parameters independently from amount of channels. The patch uses HAL correctly for the case cn > 1.
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