Iterative Phase Correlation #28146
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Changed condition to correctly route LINE_8 to Line() instead of Line2().
The condition 'line_type == 1 || line_type == 4' was causing LINE_8 (value 8)
to be processed by Line2() which implements 4-connectivity, and LINE_4 (value 4)
to be processed by Line() which was implementing 8-connectivity behavior. This
resulted in swapped line connectivity.
Changed to 'line_type == 1 || line_type == 8' so LINE_8 goes to Line() with
8-connectivity and LINE_4 goes to Line2() with 4-connectivity, matching the
documented behavior where LINE_4 should produce 4-connected lines and LINE_8
should produce 8-connected lines.
Fixes#26413
Correct minAreaRect angle to be in range [-90, 0) #28051
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Box angle range over all imgproc tests is in interval `[-90, -0.0581199]`
resolves https://github.com/opencv/opencv/issues/27667
resolves https://github.com/opencv/opencv/issues/19472
resolves https://github.com/opencv/opencv/issues/24436
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Handle near-zero convexity in convexHull #28043
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resolves https://github.com/opencv/opencv/issues/21482
closes https://github.com/opencv/opencv/issues/14401
Also skip a code that determines orientation inside rotatingCalipers and rely on the order after convexHull
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Added BitShift option to CLAHE #28014
Briefly, this PR is the fix of https://github.com/opencv/opencv/issues/28002
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imgproc: supports CV_SIMD_SCALABLE in pointSetBoundingRect #27479
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Fixed standard HoughLines output shift for rho. #27992
Closes: https://github.com/opencv/opencv/issues/25038
Replaces: https://github.com/opencv/opencv/pull/25043
Merge with https://github.com/opencv/opencv_extra/pull/1288
The original implementation introduces systematic shift (-rho/2) for odd indexes. Integer division just gives proper rounding.
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Remove performance bottleneck caused by redundant type conversions in the
initGMMs() function's tight nested loops that iterate over all image pixels.
Changes:
- Changed storage vectors from Vec3f to Vec3b to store pixel data directly
without intermediate conversion
- Modified kmeans preparation to convert Vec3b data to CV_32FC1 only when
creating the Mat for clustering (using convertTo instead of per-pixel cast)
- Explicitly convert Vec3b to Vec3d only when calling addSample() method
Performance Impact:
Previously: Vec3b -> Vec3f (in loop) -> Vec3d (implicit in addSample)
Now: Vec3b (in loop) -> Vec3d (explicit, only in addSample call)
This eliminates one unnecessary type conversion per pixel in the tight loop,
reducing computational overhead especially for large images.
Fixes#27968
Try to fix distant points to save time when ThickLine() calls FillConvexPoly() #27366
Proposal for #27365
cv::clipLine() is useful, but one should take care of a margin to preserve line caps.
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Improved blur #27822
* Perform row and column filter operations in a single pass.
* Temporary storage of intermediate results are avoided.
* Impacts 32F and 64F inputs for ksize <=5.
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imgproc: add minEnclosingConvexPolygon #27369
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Improved Gaussian Blur #27795
- Horizontal and vertical kernels of 3N131 and 5N14641 are combined for non-border(inner) regions.
- Temporary storage of intermediate results are avoided by combining the kernels.
- Further refinement of other 3N, 5N to be added later.
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Use MatShape instead of MatSize inside cv::Mat/cv::UMat #27757
**Merge together with https://github.com/opencv/opencv_contrib/pull/3996**
---
This PR continues cv::Mat/cv::UMat refactoring. See #26056, where `MatShape` was introduced. Now it's put inside cv::Mat/cv::UMat instead of a weird `MatSize`. MatSize is now an alias for MatShape:
**before:**
```
struct MatShape { ... };
struct MatSize { ... };
struct Mat {
...
int dims;
int rows;
int cols;
...
MatShape shape() const { ... /* constructs MatShape out of MatSize and returns it;
layout is always 'unknown', because we don't store it */ }
MatSize size; // size is not valid without the parent cv::Mat,
// because size.p may point to Mat::rows or to Mat::cols,
// depending on the dimensionality, and dims() returns Mat::dims.
MatStep step; // may allocate memory, depending on the dimensionality.
...
};
```
**after:**
```
struct MatShape { ... };
typedef MatShape MatSize; // they are now synonyms
struct Mat {
...
int dims;
int rows;
int cols;
...
MatShape shape() const { return size; } // just return the embedded shape (including the proper layout information)
MatSize size; // size is self-contained data structure that can be used without the parent cv::Mat.
// size.dims is now a copy of dims; size.p[*] contains copies of Mat::rows and Mat::cols when dims <= 2.
MatStep step; // does not allocate extra memory buffers.
...
};
```
There are several reasons to do that:
1. the main reason is to be able to store data layout (MatShape::layout) inside each cv::Mat/cv::UMat. This is necessary for the proper shape inference in DNN module. In particular, it's necessary for the next step of DNN inference optimization where we introduce block-layout-optimized convolution and other operations. Later on, we can use layout information to support non-interleaved images (e.g. RRR...GGG...BBB...) or even batches of such images in core/imgproc modules.
2. the other reason is to represent 3D/4D/5D etc. tensors as cv::Mat/cv::UMat instances more conveniently, without extra dynamic memory allocation. Before this patch we allocated some memory buffers dynamically to store shape & steps for more than 2D arrays. Now the whole cv::Mat/cv::UMat header can be stored completely on stack/in a container. Creating another copy of Mat/UMat header is now done more efficiently.
3. the third reason is to introduce the new coding pattern: `dst.create(src.size, <dst_type>);`. The pattern is suitable for most of element-wise (including cloning) and filtering operations. This pattern does not only look crisp and self-documenting, it will also automatically copy shape (including layout) from the source tensor into the destination matrix/tensor.
4. in the future we might add `colorspace` member to MatShape that will allow to distinguish RGB from BGR or NV12. `dst.create(src.size, <dst_type>);` will then copy the colorspace information as well.
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Set limitation to IPP Bilateral Filter tiles number to avoid too small tiles #27720
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This PR fixes the following issue in Bilateral Filter tiling in IPP integration: image ROI can't be closer to the image border than the filter window radius. This issue shows itself during separation of the image to tiles for multithreaded processing. If the tile size small enough, the second tile is closer to the upper image border than the bilateral filter radius, which leads to the incorrect result. To fix this, we need a limitation to the tile size - done in this PR.
_Note: red build status looks like unrelated to the current change_
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Subdiv2d rect2f clean #27641
Closes https://github.com/opencv/opencv/issues/27623
Changes:
- Added Subdiv2D(Rect2f) constructor overload
- Added initDelaunay(Rect2f) method overload
- No changes to the previous implementation to keep it backward compatible
- Added tests for init and testing with edge case of extremely small coordinates
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See original pull request at : https://github.com/opencv/opencv/pull/27631
imgproc: Bilateral filter performance improvement #27433
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optimize some drawing with stack allocation #27599
Some drawings can try a stack allocation instead of a std::vector
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Take into account overflow for connected components #27582
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Fix#27568
The problem was caused by a label type overflow (`debug_example.npy` contains `92103` labels, that doesn't fit in the `CV_16U` (`unsigned short`) type). If pass `CV_32S` instead of `CV_16U` as `ltype` - everything will be calculated successfully
Added overflow detection to throw exception with a clear error message instead of strange segfault/assertion error
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Fixed build issue with some old GCC versions #27569
Fixes build for GCC 4.8.5 at least. Looks like the method default signature differs cross GCC versions or newer version has softer checker.
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Issue 26972: Proper treatment of float values in intersectConvexConvex #26974
As outlined in https://github.com/opencv/opencv/issues/26972 the function `intersectConvexConvex()` may not work as expected in the corner case, where two polygons intersect at a corner. A concrete example is given that I added as unit test. The unit test would fail without the proposed bug fix. I recommend porting the fix to all versions.
Now concerning the fix: When digging into the implementation I found, that when the line intersections are computed, openCV currently does not apply floating point comparison syntax, but pretends that line end points are exact. Instead I replaced the formulation using the eps that is already used in another component of the function in line.277: `epx=1e-5`. IMO that is solid enough, definitely better than assuming an exact floating point comparison is possible.
As a follow up I would suggest to use a scalable eps, s.t. also cases with high floating point numbers would be less error prone. However that would need to be done in all relevant sub steps, not just the line intersection code. So for me outside the scope of this fix.
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imgproc: vectorize cv::createHanningWindow #27368
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Correct IPP distanceTransform results with single thread #27432
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resolves#24082
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zdenyhraz
Alexander Smorkalov
Vincent Rabaud
Alexander Smorkalov
Galina Bykova
Ghazi-raad
Dmitry Kurtaev
harunresit
penghuiho
Yuantao Feng
Samaresh Kumar Singh
Pierre Chatelier
MaximSmolskiy
ashish-sriram
SaraKuhnert
Madan mohan Manokar
Vadim Pisarevsky
Nadezhda Mizonova
Aditya Jha
ekharkov
eplankin
Martin
Souriya Trinh
Kavyansh Tyagi