1
0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 00:03:03 +04:00

Merge pull request #25268 from gursimarsingh:samples_cleanup_python

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aim to improve the repository's clarity and usability by removing examples that are no longer relevant or have been superseded by more up-to-date techniques.
This commit is contained in:
Gursimar Singh
2024-07-31 18:41:00 +05:30
committed by GitHub
parent f24e80297a
commit 3dcc8c38b4
42 changed files with 69 additions and 1711 deletions
+8 -1
View File
@@ -2160,6 +2160,10 @@ the invert function (preferably using the #DECOMP_SVD method, as the most accura
*/
CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar);
/** @example samples/python/snippets/dft.py
An example on Discrete Fourier transform (DFT) in python.
*/
/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.
The function cv::dft performs one of the following:
@@ -3055,7 +3059,10 @@ private:
//! @{
/** @example samples/cpp/snippets/kmeans.cpp
An example on K-means clustering
An example on k-means clustering
*/
/** @example samples/python/snippets/kmeans.py
An example on k-means clustering in python
*/
/** @brief Finds centers of clusters and groups input samples around the clusters.