Fix for 2 channel PNGs #27469closes#26825
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Fixing imread() function 16 bit reading png problem with libspng #27113
The purpose of the PR was to load bit-exact compatible results with libspng and libpng. To test this, `Imgcodecs_Png_PngSuite `was improved. Files containing gamma correction were moved to a separate test called `Imgcodecs_Png_PngSuite_Gamma `because the logic created for the other files did not apply to those with gamma correction. As a result, libspng now works in bit-exact compatibility with libpng. The code can be refactored later.
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cuda: Fix GpuMat::convertTo issues described in 27373 #27379
Fix https://github.com/opencv/opencv/issues/27373.
1. `GpuMat::convertTo` uses `convertToScale` due to incorrect overload.
2. There are no runtime checks to prevent the use of `CV_16U` data types in Release builds.
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Changes about when APNG has a hidden frame #27127
closes : #27074
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imgproc: vectorize cv::createHanningWindow #27368
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imread: GDAL multi-channel support #27458
- tested on 30-channel FITS and 186-channel ENVI files
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fix for the issue #27456#27457closes#27456
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Consider QRCode ECI encoding #24426
### Pull Request Readiness Checklist
related: https://github.com/opencv/opencv/pull/24350#pullrequestreview-1661658421
1. Add `getEncoding` method to obtain ECI number
2. Add `detectAndDecodeBytes`, `decodeBytes`, `decodeBytesMulti`, `detectAndDecodeBytesMulti` methods in Python (return `bytes`) and Java (return `byte[]`)
3. Allow Python bytes to std::string conversion in general and add `encode(byte[] encoded_info, Mat qrcode)` in Java
Python example with Kanji encoding:
```python
img = cv.imread("test.png")
detect = cv.QRCodeDetector()
data, points, straight_qrcode = detect.detectAndDecodeBytes(img)
print(data)
print(detect.getEncoding(), cv.QRCodeEncoder_ECI_SHIFT_JIS)
print(data.decode("shift-jis"))
```
```
b'\x82\xb1\x82\xf1\x82\xc9\x82\xbf\x82\xcd\x90\xa2\x8aE'
20 20
こんにちは世界
```
source: https://github.com/opencv/opencv/blob/ba4d6c859d21536f84e0328c16f4cc3e96bf3065/modules/objdetect/test/test_qrcode_encode.cpp#L332
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Fixed bugs in orthogonalization; simplified column vectors copying #27437
This PR mirrors to OpenCV a bug fix addressed by commit [a03d34b](https://github.com/terzakig/sqpnp/commit/a03d34b641ebba2986cf457cd910218cc8d3cc8c) in SQPnP
It also fixes bugs in the orthogonalization introduced during the porting to OpenCV and simplifies column vectors copying, eliminating double loops.
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Implemented:
- A C++ proxy class PythonCustomStreamSource that implements the
IStreamSource interface. This class acts as a bridge between
G-API’s internal streaming engine and user-defined Python
objects. Internally, it stores a reference to a Python object
(PyObject*) and is responsible for: calling the Python object’s
pull() method to retrieve the next frame, calling the descr_of()
method to obtain the frame format description, acquiring and
releasing the Python GIL as needed, converting the returned
numpy.ndarray into cv::Mat, and handling any exceptions or
conversion errors with proper diagnostics.
- A Python-facing factory function, cv.gapi.wip.make_py_src(),
which takes a Python object as an argument and wraps it into a
cv::Ptr<IStreamSource>. Internally, this function constructs a
PythonCustomStreamSource instance and passes the Python object to
it. This design allows Python users to define any class that
implements two methods: pull() and descr_of(). No subclassing or
special decorators are required on the Python side. The user
simply needs to implement the expected interface.
Co-authored-by: Leonor Francisco <leonor.francisco@tecnico.ulisboa.pt>
Close https://github.com/opencv/opencv/issues/27413
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Correct IPP distanceTransform results with single thread #27432
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resolves#24082
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Fixed Android setCameraIndex issue. #27419
Fixed camera switching issue on Android devices: When a device has more than two cameras, the setCameraIndex method failed to switch to non-default cameras.
Root cause: The original code only considered two default camera IDs when updating camera IDs, ignoring all others. This fix ensures all camera IDs are properly handled.
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Cover all seek directions in VideoCapture Java test #27421
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Deprecate copyData Parameter in UMat Construction from std::vector and Always Copy Data #27408
Overview
This PR simplifies and modernizes the construction of cv::UMat from std::vector by removing the legacy copyData parameter, always copying the data, and ensuring clearer, safer semantics. This brings UMat in line with current best practices and paves the way for the upcoming OpenCV 5.x series.
What Changed?
1. Header Documentation Update
Removed confusing or obsolete documentation about copyData and clarified the behavior:
Old: builds matrix from std::vector with or without copying the data
New: builds matrix from std::vector. The data is always copied. The copyData parameter is deprecated and will be removed in OpenCV 5.0.
2. Implementation Update
In UMat::UMat(const std::vector<_Tp>& vec, bool copyData), the copyData parameter:
Is now ignored and marked as deprecated.
Marked with CV_UNUSED(copyData) for backward compatibility and to avoid warnings.
The constructor always copies the data from the input vector, regardless of the value of copyData.
All branching logic around copyData has been removed. Any code for "not copying" was not implemented and is now dropped.
This guarantees data safety and predictable behavior.
3. Test Added
A new test construct_from_vector in test_umat_from_vector.cpp:
Verifies that UMat copies the vector data, not referencing it.
Modifies the source vector after construction to confirm that the UMat is unaffected (proving copy, not reference).
Checks matrix shape, type, and content to ensure correctness.
Why This Change?
1. Safety and Predictability
Always copying avoids dangling references and hard-to-debug lifetime issues with stack/heap-allocated vectors.
Removes an undocumented, unimplemented branch (copyData=false).
2. Backward Compatibility
The constructor signature remains for now, but the copyData parameter is marked as deprecated and ignored.
Codebases that pass the parameter will still compile and run as before (but always copy).
3. API Clarity and Maintenance
Documentation now matches the real implementation.
No misleading expectations about zero-copy.
Code is cleaner, future-proof, and easier to maintain.
4. Preparation for OpenCV 5.0
The copyData parameter is deprecated and will be removed in OpenCV 5.x.
Prepares users and downstream libraries for the planned change.
How This Helps OpenCV Users and Developers
Guarantees data safety and makes behavior explicit.
Removes legacy/ambiguous code.
Provides a clear path to OpenCV 5.x.
Minimizes future migration pain.
Ensures all users see the same, reliable behavior (copy semantics).
Refer:#27409
fix#26276: cv::VideoWriter fails writing colorless images #27153
When writing grayscale images (`isColor=false`), `cv::VideoWriter` failed as FFmpeg backend requires input frames to be in a grayscale format. Unlike other backends, FFmpeg doesn't perform this conversion internally and expects the input to be pre-converted. To fix this, I inserted a check in the `CvVideoWriter_FFMPEG_proxy::write` to convert input frames to grayscale when the input has more than 1 channel. If this is true, then the input is converted to a gray image using `cv::cvtColor` (with `cv::COLOR_BGR2GRAY`).
Additionally, as suggested in the issue comments, I have correctly propagated the return value of `CvVideoWriter_FFMPEG::writeFrame` back to the `CvVideoWriter_FFMPEG_proxy::write`. This return value wasn't being used, and `writeFrame` was always returning false since the FFmeg's return code `AVERROR(EAGAIN)` was mistakenly being treated as an error. Now it's handled as a signal for additional input (current input was successfully written, and a new frame should be sent. [See FFmpeg documentation for `avcodec_receive_packet`](https://ffmpeg.org/doxygen/6.1/group__lavc__decoding.html)). A warning is displayed if `CvVideoWriter_FFMPEG::writeFrame` returns false. Alternatively, this could be propagated back up to the user, making cv::VideoWriter::write a boolean.
Finally, I added a test case to verify if the grayscale conversion is being done correctly.
Fixes#26276
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Fix NaNs in HDR Triangle Weights and Tonemapping and Update LDR Ground Truth in tutorial #27396
The PR closes#27392
Updated the triangle weights to use a small epsilon value instead of zero to prevent NaN issues in HDR processing.
Also fixed a float-to-double division issue by explicitly casting double values to float, which was previously producing garbage values and leading to NaNs in tonemapping.
The current LDR ground truth image used in the tutorial [ldr.png](https://github.com/opencv/opencv/blob/4.x/doc/tutorials/others/images/ldr.png) was originally generated using TonemapDurand (check this commit https://github.com/opencv/opencv/commit/833f8d16fab5e57c5e800a55fa0fb08c7a31c3b1), which was moved to opencv_contrib a long time ago in this commit: https://github.com/opencv/opencv/commit/742f22c09bd0c27b450f141bc984f280c8cde98e. However, the current Tonemap implementation in OpenCV main only performs normalization and gamma correction, which produces noticeably different results. This PR updates the LDR grouth truth image in tutorial with the result of TonemapDrago, and tutorials to use TonemapDrago as Tonemap gives a darker image.
Tonemap output:
TonemapDrago output:
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Check MS Media Foundation availability in G-API too #27355
Tries to address https://github.com/opencv/opencv-python/issues/771
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imgcodecs: jpegxl: support lossless compression #27384
Close https://github.com/opencv/opencv/issues/27382
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Add HoughCirclesWithAccumulator binding #27389
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Fix#27377
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Fix#25696: Solved the problem in Subdiv2D, empty delaunay triangulation #27149
Detailed description
Expected behaviour:
Given 4 points, where no three points are collinear, the Delaunay Triangulation Algorithm should return 2 triangles.
Actual:
The algorithm returns zero triangles in this particular case.
Fix:
The radius of the circumcircle tends to infinity when the points are closer to form collinear points, so the problem occurs because the super-triangles are not large enough,
which then results in certain edges are not swapped. The proposed solution just increases the super triangle, duplicating the value of constant for example.
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Feature: Add OpenVINO NPU support #27363
## Why
- OpenVINO now supports inference on integrated NPU devices in intel's Core Ultra series processors.
- Sometimes as fast as GPU, but should use considerably less power.
## How
- The NPU plugin is now available as "NPU" in openvino `ov::Core::get_available_devices()`.
- Removed the guards and checks for NPU in available targets for Inference Engine backend.
## Test example
### Pre-requisites
- Intel [Core Ultra series processor](https://www.intel.com/content/www/us/en/products/details/processors/core-ultra/edge.html#tab-blade-1-0)
- [Intel NPU driver](https://github.com/intel/linux-npu-driver/releases)
- OpenVINO 2023.3.0+ (Tested on 2025.1.0)
### Example
```cpp
#include <opencv2/dnn.hpp>
#include <iostream>
int main(){
cv::dnn::Net net = cv::dnn::readNet("../yolov8s-openvino/yolov8s.xml", "../yolov8s-openvino/yolov8s.bin");
cv::Size net_input_shape = cv::Size(640, 480);
std::cout << "Setting backend to DNN_BACKEND_INFERENCE_ENGINE and target to DNN_TARGET_NPU" << std::endl;
net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(cv::dnn::DNN_TARGET_NPU);
cv::Mat image(net_input_shape, CV_8UC3);
cv::randu(image, cv::Scalar(0, 0, 0), cv::Scalar(255, 255, 255));
cv::Mat blob = cv::dnn::blobFromImage(
image, 1, net_input_shape, cv::Scalar(0, 0, 0), true, false, CV_32F);
net.setInput(blob);
std::cout << "Running forward" << std::endl;
cv::Mat result = net.forward();
std::cout << "Output shape: " << result.size << std::endl; // Output shape: 1 x 84 x 6300
}
```
model files [here](https://limewire.com/d/bPgiA#BhUeSTBnMc)
docker image used to build opencv: [ghcr.io/mro47/opencv-builder](https://github.com/MRo47/opencv-builder/blob/main/Dockerfile)
Closes#26240
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Fix#27352: Add checks before getting latest pin in Net::Impl::getLatestLayerPin() #27353
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Fixes#27352
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Improve solveCubic accuracy #27347
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Fix#27323
```
2e-13 * x^3 + x^2 - 2 * x + 1 = 0 -> x^3 + 5e12 * x^2 - 1e13 * x + 5e12 = 0
```
The problem that coefficients have quite big magnitudes and current calculations are subject to round-off error
```
Q = (a1 * a1 - 3 * a2) * (1./9)
R = (2 * a1 * a1 * a1 - 9 * a1 * a2 + 27 * a3) * (1./54)
Qcubed = Q * Q * Q = a1^6/729 - (a1^4 a2)/81 + (a1^2 a2^2)/27 - a2^3/27
R * R = R^2 = a1^6/729 - (a1^4 a2)/81 + (a1^2 a2^2)/36 + (a1^3 a3)/27 - (a1 a2 a3)/6 + a3^2/4
d = Qcubed - R * R
```
Let `a1`, `a2`, `a3` have quite big same magnitudes, then we see that `Qcubed` and `R * R` have same terms `a1^6/729` and `-(a1^4 a2)/81` (which will be reduced in `d`), but they level out the other terms (these terms have `6`th and `5`th degree and other terms - less or equal than `4`th degree).
So, if these terms will participate in the calculation, this will lead to a huge round-off error.
But if we expand the expression, then round-off error should be less
```
d = Qcubed - R * R = 1/108 (a1^2 a2^2 - 4 a2^3 - 4 a1^3 a3 + 18 a1 a2 a3 - 27 a3^2)
```
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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
- [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.
- [x] The feature is well documented and sample code can be built with the project CMake
Alexander Smorkalov
Alexander Smorkalov
Suleyman TURKMEN
cudawarped
Yuantao Feng
xaos-cz
Sergei Nikiforov
Dmitry Kurtaev
Manolis Lourakis
sight
Rita Melo
Kumataro
eplankin
Kavyansh Tyagi
phanirithvij
FleeOvernight
Francisco Mónica
Abhishek Gola
s-trinh
Vincent Rabaud
Maxim Smolskiy
Liane Lin
Myron Rodrigues
Maksim Shabunin
