Feature: weighted Hough Transform #21407
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Support C++20 standard #26590
Close https://github.com/opencv/opencv/issues/26589
Related https://github.com/opencv/opencv_contrib/pull/3842
Related: https://github.com/opencv/opencv/issues/20269
- do not arithmetic enums and ( different enums or floating numeric)
- remove unused variable
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Update Documentation #26260
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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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#25006#25314
This pull request removes hed_pretrained caffe model to the SOTA dexined onnx model for edge detection. Usage of conventional methods like canny has also been added
The obsolete cpp and python sample has been removed
TODO:
- [ ] Remove temporary hack for quantized models. Refer issue https://github.com/opencv/opencv_zoo/issues/273
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Added xxxApprox overloads for YUV color conversions in HAL and AlgorithmHint to cvtColor #25932
The xxxApprox to implement HAL functions with less bits for arithmetic of FP.
The hint was introduced in #25792 and #25911
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Removed obsolete python samples #25268
Clean Samples #25006
This PR removes 36 obsolete python samples from the project, as part of an effort to keep the codebase clean and focused on current best practices. Some of these samples will be updated with latest algorithms or will be combined with other existing samples.
Removed Samples:
> browse.py
camshift.py
coherence.py
color_histogram.py
contours.py
deconvolution.py
dft.py
dis_opt_flow.py
distrans.py
edge.py
feature_homography.py
find_obj.py
fitline.py
gabor_threads.py
hist.py
houghcircles.py
houghlines.py
inpaint.py
kalman.py
kmeans.py
laplace.py
lk_homography.py
lk_track.py
logpolar.py
mosse.py
mser.py
opt_flow.py
plane_ar.py
squares.py
stitching.py
text_skewness_correction.py
texture_flow.py
turing.py
video_threaded.py
video_v4l2.py
watershed.py
These changes aim to improve the repository's clarity and usability by removing examples that are no longer relevant or have been superseded by more up-to-date techniques.
Added flag to GaussianBlur for faster but not bit-exact implementation #25792
Rationale:
Current implementation of GaussianBlur is almost always bit-exact. It helps to get predictable results according platforms, but prohibits most of approximations and optimization tricks.
The patch converts `borderType` parameter to more generic `flags` and introduces `GAUSS_ALLOW_APPROXIMATIONS` flag to allow not bit-exact implementation. With the flag IPP and generic HAL implementation are called first. The flag naming and location is a subject for discussion.
Replaces https://github.com/opencv/opencv/pull/22073
Possibly related issue: https://github.com/opencv/opencv/issues/24135
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Move API focused C++ samples to snippets #25252
Clean Samples #25006
This PR removes 39 outdated C++ samples from the project, as part of an effort to keep the codebase clean and focused on current best practices.
Add a new function that approximates the polygon bounding a convex hull with a certain number of sides #25607
merge PR with <https://github.com/opencv/opencv_extra/pull/1179>
This PR is based on the paper [View Frustum Optimization To Maximize Object’s Image Area](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1fbd43f3827fffeb76641a9c5ab5b625eb5a75ba).
# Problem
I needed to reduce the number of vertices of the convex hull so that the additional area was minimal, andall vertices of the original contour enter the new contour.
# Description
Initially in the contour of n vertices, at each stage we consider the intersection points of the lines formed by each adjacent edges. Each of these intersection points will form a triangle with vertices through which lines pass. Let's choose a triangle with the minimum area and merge the two vertices at the intersection point. We continue until there are more vertices than the specified number of sides of the approximated polygon.
# Complexity:
Using a std::priority_queue or std::set time complexity is **(O(n\*ln(n))**, memory **O(n)**,
n - number of vertices in convex hull.
count of sides - the number of points by which we must reduce.
## Comment
If epsilon_percentage more 0, algorithm can return more values than _side_.
Algorithm returns OutputArray. If OutputArray.type() equals 0, algorithm returns values with InputArray.type().
New test uses image which are not in opencv_extra, needs to be added.
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Relates to #24603
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YUV codes for cvtColor: descriptions added #25616
This PR contains descriptions for various RGB <-> YUV color conversion codes as well as detailed comments in the source code.
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It is said "see above" because calcBackProject is listed under calcHist function in source file, while it is listed before due to the lexicographic ordering.
imgproc: refactor EMD to reduce C-API usage #25469
- added more tests for EMD
- refactored to remove CvArr
- used BufferArea for memory allocations
- renamed functions and variables and formatted the code
- kept legacy functions intact in separate header
Geometry C++ sample combining other shape detection samples #25304
Clean Samples #25006
This PR removes adds a new cpp sample (geometry) which combines different methods of finding and drawing shapes in an image. It makes separate samples for convexHull, fitellipse, minAreaRect, minAreaCircle redudant. Shapes can be changed using hotkeys after running the program
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Reworked findContours to reduce C-API usage #25146
What is done:
* rewritten `findContours` and `icvApproximateChainTC89` using C++ data structures
* extracted LINK_RUNS mode to separate new public functions - `findContoursLinkRuns` (it uses completely different algorithm)
* ~added new public `cv::approximateChainTC89`~ - **❌ decided to hide it**
* enabled chain code output (method = 0, no public enum value for this in C++ yet)
* kept old function as `findContours_old` (exported, but not exposed to user)
* added more tests for findContours (`test_contours_new.cpp`), some tests compare results of old function with new one. Following tests have been added:
* contours of random rectangle
* contours of many small (1-2px) blobs
* contours of random noise
* backport of old accuracy test
* separate test for LINK RUNS variant
What is left to be done (can be done now or later):
* improve tests:
* some tests have limited verification (e.g. only verify contour sizes)
* perhaps reference data can be collected and stored
* maybe more test variants can be added (?)
* add enum value for chain code output and a method of returning starting points (e.g. first 8 elements of returned `vector<uchar>` can represent 2 int point coordinates)
* add documentation for new functions - **✔️ DONE**
* check and improve performance (my experiment showed 0.7x-1.1x some time ago)
* remove old functions completely (?)
* change contour return order (BFS) or allow to select it (?)
* return result tree as-is (?) (new data structures should be exposed, bindings should adapt)
Documentation transition to fresh Doxygen #25042
* current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584)
* Doxyfile adapted to new version
* MathJax updated to 3.x
* `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings)
* refactored matx.hpp - extracted matx.inl.hpp
* opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
C-API cleanup: apps, imgproc_c and some constants #25075
Merge with https://github.com/opencv/opencv_contrib/pull/3642
* Removed obsolete apps - traincascade and createsamples (please use older OpenCV versions if you need them). These apps relied heavily on C-API
* removed all mentions of imgproc C-API headers (imgproc_c.h, types_c.h) - they were empty, included core C-API headers
* replaced usage of several C constants with C++ ones (error codes, norm modes, RNG modes, PCA modes, ...) - most part of this PR (split into two parts - all modules and calib+3d - for easier backporting)
* removed imgproc C-API headers (as separate commit, so that other changes could be backported to 4.x)
Most of these changes can be backported to 4.x.
First proposal of cv::remap with relative displacement field (#24603) #24621
Implements #24603
Currently, `remap()` is applied as `dst(x, y) <- src(mapX(x, y), mapY(x, y))` It means that the maps must be filled with absolute coordinates.
However, if one wants to remap something according to a displacement field ("warp"), the operation should be `dst(x, y) <- src(x+displacementX(x, y), y+displacementY(x, y))`
It is trivial to build a mapping from a displacement field, but it is an undesirable overhead for CPU and memory.
This PR implements the feature as an experimental option, through the optional flag WARP_RELATIVE_MAP than can be ORed to the interpolation mode.
Since the xy maps might be const, there is no attempt to add the coordinate offset to those maps, and everything is postponed on-the-fly to the very last coordinate computation before fetching `src`. Interestingly, this let `cv::convertMaps()` unchanged since the fractional part of interpolation does not care of the integer coordinate offset.
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Implement color conversion from RGB to YUV422 family #24333
Related PR for extra: https://github.com/opencv/opencv_extra/pull/1104
Hi,
This patch provides CPU and OpenCL implementations of color conversions from RGB/BGR to YUV422 family (such as UYVY and YUY2).
These features would come in useful for enabling standard RGB images to be supplied as input to algorithms or networks that make use of images in YUV422 format directly (for example, on resource constrained devices working with camera images captured in YUV422).
The code, tests and perf tests are all written following the existing pattern. There is also an example `bin/example_cpp_cvtColor_RGB2YUV422` that loads an image from disk, converts it from BGR to UYVY and then back to BGR, and displays the result as a visual check that the conversion works.
The OpenCL performance for the forward conversion implemented here is the same as the existing backward conversion on my hardware. The CPU implementation, unfortunately, isn't very optimized as I am not yet familiar with the SIMD code.
Please let me know if I need to fix something or can make other modifications.
Thanks!
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Alexander Smorkalov
MaximSmolskiy
Masahiro Ogawa
Letu Ren
Kumataro
Yuantao Feng
Maksim Shabunin
Alexander Smorkalov
Suleyman TURKMEN
Gursimar Singh
Alexander Smorkalov
Junyan721113
Mironov Arseny
Pierre Chatelier
Rostislav Vasilikhin
xndcn
Yusuke Kameda
Kirin Chu
definitelyuncertain