When computing TM_SQDIFF for 8-bit images, crossCorr stores intermediate
results in CV_32F which loses precision for large patch sums (e.g. 25x25
patch of value 255 gives sum=40603125, exceeding float32's ~7 significant
digits). This causes the final SQDIFF result to have incorrect non-zero
values even when image and template are identical.
Fix: use a CV_64F intermediate buffer for crossCorr and common_matchTemplate
when depth==CV_8U and method is TM_SQDIFF or TM_SQDIFF_NORMED, then convert
back to CV_32F for the final output.
Fixes#21786
imgproc: fix missing tipLength validation in arrowedLine #29191
### Pull Request Description
**Issue/Bug:**
Currently, the `cv::arrowedLine` function in `modules/imgproc/src/drawing.cpp` lacks boundary validation for the `tipLength` parameter. Passing an invalid ratio (e.g., negative values or values > 1.0) leads to mathematically distorted arrowhead coordinates being silently passed to the underlying `line()` drawing function, resulting in severely deformed output without any warning.
**Solution:**
Added a `CV_Assert` to enforce the geometric contract of `tipLength`, ensuring it strictly falls within the logical bounds `(0.0, 1.0]`. This adheres to the fail-fast principle and prevents silent geometric failures.
**Testing:**
Successfully built the project locally and added a dedicated regression test (`arrowedLine_tipLength_validation`) using Google Test in `modules/imgproc/test/test_drawing.cpp`. Tested with boundary values (e.g., `tipLength = 1.5` and `-0.5`), and the assertion correctly intercepts the invalid parameters before rasterization.
*(Note: This contribution is part of a university engineering project evaluating open-source workflow and C++ robustness.)*
imgproc: add findTRUContours - lock-free parallel contour extraction #28773
Integrates the TRUCO algorithm (Threaded Raster Unrestricted Contour Ownership, @cite TRUCO2026) as a transparent fast path inside `cv::findContours`. No API change is required: existing code automatically benefits when `mode=RETR_LIST` and no hierarchy output is requested.
### How it works
When `mode=RETR_LIST` and the caller does not request hierarchy, `findContours` delegates to the TRUCO parallel engine instead of Suzuki-Abe. All ContourApproximationModes are supported; approximation is applied in parallel after extraction. In all other cases the original Suzuki-Abe path is used unchanged.
### Key design ideas
- Row-strip domain decomposition parallelised via `cv::parallel_for_`
- Start-point ownership rule + speculative downward tracing eliminate tile stitching and synchronisation primitives (lock-free)
- 8-bit state space instead of the 32-bit integer labeling required by Suzuki-Abe, giving higher SIMD throughput and lower memory-bandwidth pressure
- Paged contour buffer (`TRUCOPagedContour`) avoids heap reallocation on the hot tracing path
- SIMD-accelerated row scanning via `cv::v_uint8` intrinsics
- Thread count controlled globally via `cv::setNumThreads()`, consistent with OpenCV conventions
### Output correctness
Significant effort has gone into ensuring the output is identical to the original `findContours` in every respect: same contour set, same ordering, and same approximation results for all methods. A dedicated test suite (`test_contours_truco.cpp`) verifies exact match against Suzuki-Abe across all four `ContourApproximationModes` and thread counts from 1 to 39, using noise images, circles, nested rectangles, and mixed scenes.
### Performance vs. Suzuki-Abe (from submitted paper)
- single-thread: ~1.8–1.9× faster (8-bit SIMD advantage)
- 20 threads: ~14–20× faster on i7-13700H (6P+8E, 20T)
- 20 threads: ~10–12× faster on Xeon Silver 4510 (12C/24T)
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fix: Guard ndsrvp filter center copy when source span is empty #28915
## Summary
This adds a defensive guard around the centre-region `memcpy` in the ndsrvp HAL filter padding path.
**Description**: Multiple memcpy calls in the ndsrvp HAL filter, bilateral filter, and median blur routines use computed sizes (e.g., cnes * (rborder - j), cn * (rborder - j), src_step * height) without validating that the computed byte count fits within the destination buffer. An attacker supplying a crafted image with manipulated dimensions, channel counts, or border parameters can cause the computed copy size to exceed the allocated destination buffer, resulting in a heap buffer overflow that corrupts adjacent memory.
## Changes
- `hal/ndsrvp/src/filter.cpp`
## Verification
- [x] Build passes
- [x] Scanner re-scan confirms fix
- [x] LLM code review passed
---
*Automated security fix by [OrbisAI Security](https://orbisappsec.com)*
fitEllipseDirect: replace det(M) threshold with eigenvector quality check
- Add Ts ≈ 0 guard to avoid division by zero in Schur complement
- Move eigenNonSymmetric inside perturbation loop
- Validate eigenvector with 4ac-b² > 1e-6*||v||² to filter garbage from
complex eigenvalues
fitEllipseAMS: scale threshold by 1/n^5 to match det(M) magnitude
Add Imgproc_FitEllipseDirect_NearCircular regression test
Fix IPP warpAffine #28562
The issue is that we compute the inverse transformation and pass that to the HAL, but tell IPP that we use the actual transformation.
https://github.com/opencv/opencv/blob/a269a489b94b84717691533a04f79d9a0e5f479a/modules/imgproc/src/imgwarp.cpp#L2399-L2412
I have added an additional test with `CV_16S` Mats to test the IPP implementation, because right now no other `warpAffine` test enters the IPP branch.
This fixes#28554.
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Fix integer overflow in medianBlur #28386
The bug in #28385 is that the pointer arithmetic in
https://github.com/opencv/opencv/blob/d8bc5b94b851d5b392c61e5b954fba992e18bb73/modules/imgproc/src/median_blur.simd.hpp#L666-L668
overflows if `i*sstep` is larger than `INT_MAX`, because both variables are of type `int`. If one operand is of type `size_t` instead, the other operand gets promoted to `size_t` before multiplication and overflow doesn't happen anymore.
The test allocates 2.3GB for the Mat; I hope that's okay.
This PR fixes#28385
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docs(imgproc): clarify cv::moments behavior for degenerate contours #28227
relates to https://github.com/opencv/opencv/issues/28222
Clarifies that for degenerate contours (single point or collinear points),
cv::moments() returns m00 == 0 and centroid is undefined.
Documents common workarounds such as boundingRect center or point averaging.
Fix the out-of-bounds read in cv::bilateralFilter for 32f images #28259
### Root Cause Analysis
The issue was caused by a discrepancy between the image range used to allocate the color weight look-up table (LUT) and the actual range of pixel values encountered during filtering, especially near the image borders.
- Range Computation: `cv::bilateralFilter` computes the min/max values of the source image and allocates a `LUT (expLUT)` of size `kExpNumBins + 2` based on this range.
- Border Padding: If `cv::BORDER_CONSTANT` is used (defaulting to 0), and 0 is outside the image's original range (e.g., an image with values between 100 and 200), the padded image will contain values (0) that create differences larger than those accounted for in the `LUT`.
- Out-of-Bounds Access: When calculating the color weight, the code computes an index `idx` from the absolute difference. If this difference exceeds the expected range, `idx` can reach or exceed `kExpNumBins + 1`. Since the code performs linear interpolation using `expLUT[idx]` and `expLUT[idx + 1]`, an `idx` of `kExpNumBins + 1` causes an access to `expLUT[kExpNumBins + 2]`, which is out of bounds.
### Fix
I implemented a robust clamping mechanism in both the SIMD (AVX/SSE) and scalar paths of the bilateral filter invoker:
- Signature Update: Updated `bilateralFilterInvoker_32f` to accept `kExpNumBins` (the maximum valid `LUT` index).
- Clamping: Clamped the computed color difference (alpha) to `kExpNumBins` before calculating the `LUT` index. This ensures that any difference exceeding the planned range is safely treated as the maximum difference in the `LUT` (which usually corresponds to a weight of 0), avoiding any out-of-bounds memory access.
### Modified Files
Modified Files:
`modules/imgproc/src/bilateral_filter.simd.hpp`: Updated the invoker class and SIMD/scalar loops to clamp the LUT index.
`modules/imgproc/src/bilateral_filter.dispatch.cpp`: Updated the dispatch call site to pass the correct LUT size.
Closes#28254
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imgproc: fix heap-buffer-overflow in stackBlur #28233#28250
### Summary
Fixes a heap-buffer-overflow in `cv::stackBlur` when the kernel size is larger than the image dimensions
### Changes
* Added input validation to clamp the kernel size to the image dimensions.
* Added a regression test (`regression_28233`) covering 1x1 and small image cases.
Fixes#28233
Keep convexHull output indices monotone if possible #28163
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/24907 ?
resolves https://github.com/opencv/opencv/issues/4954
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Fixed issues identified by PVS Studio #28185
Partially fixes https://github.com/opencv/opencv/issues/28167
Paper: https://pvs-studio.com/en/blog/posts/cpp/1321/
Closed items: N2, N4, N5, N6, N7, N8, N10, N11, N13, N14.
To be continued...
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Iterative Phase Correlation #28146
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Test: Add regression test for LINE_4 vs LINE_8 connectivity #28120
Add test to verify correct behavior of LINE_4 (4-connected) and LINE_8 (8-connected) line drawing. This test ensures:
- LINE_4 produces staircase pattern (more pixels) for diagonal lines
- LINE_8 produces diagonal steps (fewer pixels)
- LINE_4 pixels have only horizontal/vertical neighbors (no diagonal-only)
Regression test for issue #26413 where LINE_4 and LINE_8 behaviors were swapped.
Correct minAreaRect angle to be in range [-90, 0) #28051
### Pull Request Readiness Checklist
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
### Pull Request Readiness Checklist
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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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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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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Refactor minEnclosingCircle tests #27900
### Pull Request Readiness Checklist
Separate input points for tests
Before this, next input points depended on previous ones and it was not obvious which input points specific test checked
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imgproc: add minEnclosingConvexPolygon #27369
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Improve fitEllipseDirect tests #27717
### Pull Request Readiness Checklist
Previous `fit_and_check_ellipse` implementation was very weak - it only checks that points center lies inside ellipse.
Current implementation `fit_and_check_ellipse` checks that points RMS (Root Mean Square) algebraic distance is quite small. It means that on average points are near boundary of ellipse. Because for points on ellipse algebraic distance is equal to `0` and for points that are close to boundary of ellipse is quite small
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Fix checking that point lies inside ellipse #27704
### Pull Request Readiness Checklist
Previous `check_pt_in_ellipse` implementation was incorrect. For points on ellipse `cv::norm(to_pt)` should be equal to `el_dist`.
I tested current implementation with following Python script:
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
def check_pt_in_ellipse(pt, el):
center, axes, angle = ellipse
to_pt = pt - center
el_angle = angle * np.pi / 180
to_pt_r_x = to_pt[0] * np.cos(-el_angle) - to_pt[1] * np.sin(-el_angle)
to_pt_r_y = to_pt[0] * np.sin(-el_angle) + to_pt[1] * np.cos(-el_angle)
pt_angle = np.arctan2(to_pt_r_y / axes[1], to_pt_r_x / axes[0])
x_dist = 0.5 * axes[0] * np.cos(pt_angle)
y_dist = 0.5 * axes[1] * np.sin(pt_angle)
el_dist = np.sqrt(x_dist * x_dist + y_dist * y_dist)
assert abs(np.linalg.norm(to_pt) - el_dist) < 1e-10
# TEST(Imgproc_FitEllipse_Issue_4515, accuracy) {
points = np.array([
[327, 317],
[328, 316],
[329, 315],
[330, 314],
[331, 314],
[332, 314],
[333, 315],
[333, 316],
[333, 317],
[333, 318],
[333, 319],
[333, 320],
])
ellipse = cv2.fitEllipseDirect(points)
center, axes, angle = ellipse
angle_rad = np.deg2rad(angle)
points_on_ellipse = []
for point_angle_deg in range(0, 360, 10):
point_angle = np.deg2rad(point_angle_deg)
point = np.array([0., 0.])
point_x = axes[0] * 0.5 * np.cos(point_angle)
point_y = axes[1] * 0.5 * np.sin(point_angle)
point[0] = point_x * np.cos(angle_rad) - point_y * np.sin(angle_rad)
point[1] = point_x * np.sin(angle_rad) + point_y * np.cos(angle_rad)
point[0] += center[0]
point[1] += center[1]
points_on_ellipse.append(point)
points_on_ellipse = np.array(points_on_ellipse)
for point in points_on_ellipse:
check_pt_in_ellipse(point, ellipse)
plt.figure(figsize=(8, 8))
plt.scatter(points[:, 0], points[:, 1], c='red', label='points')
plt.scatter(points_on_ellipse[:, 0], points_on_ellipse[:, 1], c='yellow', label='ellipse')
ellipse = Ellipse(xy=center, width=axes[0], height=axes[1],
angle=angle, facecolor='none', edgecolor='b')
plt.gca().add_patch(ellipse)
plt.gca().set_aspect('equal')
plt.legend()
plt.show()
```
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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
Take into account overflow for connected components #27582
### Pull Request Readiness Checklist
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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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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Correct IPP distanceTransform results with single thread #27432
### Pull Request Readiness Checklist
resolves#24082
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Alexander Smorkalov
Rishiii57
WITHOUTNAMESES
Vincent Rabaud
Alexander Smorkalov
Maksim Shabunin
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orbisai0security
Ismail
gideok Kim
Adrian Kretz
raimbekovm
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satyam yadav
Dmitry Kurtaev
zdenyhraz
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Galina Bykova
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MaximSmolskiy
SaraKuhnert
Aditya Jha
Martin
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