Merge pull request #26002 from nishanthdass:doc/missing-fields-python-tutorials

Remove empty Additional Resources and Exercises fields from tutorials #26002 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake This PR is in response to issue [26001](https://github.com/opencv/opencv/issues/26001) This pull request addresses the issue of empty "Additional Resources" and "Exercises" fields in several OpenCV-Python tutorials. The empty sections have been removed to improve the clarity and consistency of the documentation.
2024-11-27 20:50:25 +08:00 · 2024-08-09 03:34:44 -04:00 · 2024-08-09 03:34:44 -04:00 · 6cd730a02c
commit 6cd730a02c
parent c9df679943
42 changed files with 0 additions and 174 deletions
--- a/doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown
+++ b/doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown
@ -216,8 +216,6 @@ for i in range(len(objpoints)):

 print( "total error: {}".format(mean_error/len(objpoints)) )
@endcode
-Additional Resources
--------------------

 Exercises
 ---------
--- a/doc/py_tutorials/py_calib3d/py_epipolar_geometry/py_epipolar_geometry.markdown
+++ b/doc/py_tutorials/py_calib3d/py_epipolar_geometry/py_epipolar_geometry.markdown
@ -158,9 +158,6 @@ side. That meeting point is the epipole.

 For better results, images with good resolution and many non-planar points should be used.

-Additional Resources
--------------------
-
 Exercises
 ---------

--- a/doc/py_tutorials/py_calib3d/py_pose/py_pose.markdown
+++ b/doc/py_tutorials/py_calib3d/py_pose/py_pose.markdown
@ -119,9 +119,3 @@ And look at the result below:

 If you are interested in graphics, augmented reality etc, you can use OpenGL to render more
 complicated figures.
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown
+++ b/doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown
@ -195,9 +195,3 @@ See the result below. (Image is displayed with matplotlib. So RED and BLUE chann
 interchanged):

 ![image](images/border.jpg)
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_core/py_image_arithmetics/py_image_arithmetics.markdown
+++ b/doc/py_tutorials/py_core/py_image_arithmetics/py_image_arithmetics.markdown
@ -110,9 +110,6 @@ img2_fg.

 ![image](images/overlay.jpg)

-Additional Resources
--------------------
-
 Exercises
 ---------

--- a/doc/py_tutorials/py_core/py_optimization/py_optimization.markdown
+++ b/doc/py_tutorials/py_core/py_optimization/py_optimization.markdown
@ -163,6 +163,3 @@ Additional Resources
 2.  Scipy Lecture Notes - [Advanced
    Numpy](http://scipy-lectures.github.io/advanced/advanced_numpy/index.html#advanced-numpy)
 3.  [Timing and Profiling in IPython](http://pynash.org/2013/03/06/timing-and-profiling/)
-
-Exercises
---------
--- a/doc/py_tutorials/py_feature2d/py_fast/py_fast.markdown
+++ b/doc/py_tutorials/py_feature2d/py_fast/py_fast.markdown
@ -138,6 +138,3 @@ Additional Resources
 2.  Edward Rosten, Reid Porter, and Tom Drummond, "Faster and better: a machine learning approach to
    corner detection" in IEEE Trans. Pattern Analysis and Machine Intelligence, 2010, vol 32, pp.
    105-119.
-
-Exercises
---------
--- a/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown
+++ b/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown
@ -102,9 +102,3 @@ plt.imshow(img3, 'gray'),plt.show()
 See the result below. Object is marked in white color in cluttered image:

 ![image](images/homography_findobj.jpg)
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_feature2d/py_features_meaning/py_features_meaning.markdown
+++ b/doc/py_tutorials/py_feature2d/py_features_meaning/py_features_meaning.markdown
@ -81,9 +81,3 @@ or do whatever you want.

 So in this module, we are looking to different algorithms in OpenCV to find features, describe them,
 match them etc.
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown
+++ b/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown
@ -209,9 +209,3 @@ plt.imshow(img3,),plt.show()
 See the result below:

 ![image](images/matcher_flann.jpg)
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_feature2d/py_orb/py_orb.markdown
+++ b/doc/py_tutorials/py_feature2d/py_orb/py_orb.markdown
@ -93,6 +93,3 @@ Additional Resources

 -#  Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary R. Bradski: ORB: An efficient alternative to
    SIFT or SURF. ICCV 2011: 2564-2571.
-
-Exercises
---------
--- a/doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
+++ b/doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
@ -67,9 +67,3 @@ See the result below:
 ![image](images/shitomasi_block1.jpg)

 This function is more appropriate for tracking. We will see that when its time comes.
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
+++ b/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
@ -160,9 +160,3 @@ Here kp will be a list of keypoints and des is a numpy array of shape

 So we got keypoints, descriptors etc. Now we want to see how to match keypoints in different images.
 That we will learn in coming chapters.
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown
+++ b/doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown
@ -155,9 +155,3 @@ Finally we check the descriptor size and change it to 128 if it is only 64-dim.
 (47, 128)
@endcode
 Remaining part is matching which we will do in another chapter.
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_gui/py_mouse_handling/py_mouse_handling.markdown
+++ b/doc/py_tutorials/py_gui/py_mouse_handling/py_mouse_handling.markdown
@ -101,8 +101,6 @@ while(1):

 cv.destroyAllWindows()
@endcode
-Additional Resources
--------------------

 Exercises
 ---------
--- a/doc/py_tutorials/py_gui/py_video_display/py_video_display.markdown
+++ b/doc/py_tutorials/py_gui/py_video_display/py_video_display.markdown
@ -152,9 +152,3 @@ cap.release()
 out.release()
 cv.destroyAllWindows()
@endcode
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.markdown
+++ b/doc/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.markdown
@ -103,9 +103,6 @@ Now you take [H-10, 100,100] and [H+10, 255, 255] as the lower bound and upper b
 from this method, you can use any image editing tools like GIMP or any online converters to find
 these values, but don't forget to adjust the HSV ranges.

-Additional Resources
--------------------
-
 Exercises
 ---------

--- a/doc/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.markdown
+++ b/doc/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.markdown
@ -199,9 +199,3 @@ righty = int(((cols-x)*vy/vx)+y)
 cv.line(img,(cols-1,righty),(0,lefty),(0,255,0),2)
@endcode
 ![image](images/fitline.jpg)
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_contours/py_contour_properties/py_contour_properties.markdown
+++ b/doc/py_tutorials/py_imgproc/py_contours/py_contour_properties/py_contour_properties.markdown
@ -114,9 +114,6 @@ For eg, if I apply it to an Indian map, I get the following result :

 ![image](images/extremepoints.jpg)

-Additional Resources
--------------------
-
 Exercises
 ---------

--- a/doc/py_tutorials/py_imgproc/py_contours/py_contours_begin/py_contours_begin.markdown
+++ b/doc/py_tutorials/py_imgproc/py_contours/py_contours_begin/py_contours_begin.markdown
@ -88,9 +88,3 @@ the contour array (drawn in blue color). First image shows points I got with cv.
 much memory it saves!!!

 ![image](images/none.jpg)
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_contours/py_contours_hierarchy/py_contours_hierarchy.markdown
+++ b/doc/py_tutorials/py_imgproc/py_contours/py_contours_hierarchy/py_contours_hierarchy.markdown
@ -212,9 +212,3 @@ array([[[ 7, -1,  1, -1],
        [ 8,  0, -1, -1],
        [-1,  7, -1, -1]]])
@endcode
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_contours/py_contours_more_functions/py_contours_more_functions.markdown
+++ b/doc/py_tutorials/py_imgproc/py_contours/py_contours_more_functions/py_contours_more_functions.markdown
@ -124,9 +124,6 @@ See, even image rotation doesn't affect much on this comparison.
 moments invariant to translation, rotation and scale. Seventh one is skew-invariant. Those values
 can be found using **cv.HuMoments()** function.

-Additional Resources
-====================
-
 Exercises
 ---------

--- a/doc/py_tutorials/py_imgproc/py_filtering/py_filtering.markdown
+++ b/doc/py_tutorials/py_imgproc/py_filtering/py_filtering.markdown
@ -150,6 +150,3 @@ Additional Resources
 --------------------

 -#  Details about the [bilateral filtering](http://people.csail.mit.edu/sparis/bf_course/)
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.markdown
+++ b/doc/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.markdown
@ -163,6 +163,3 @@ Additional Resources
 --------------------

 -#  "Computer Vision: Algorithms and Applications", Richard Szeliski
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown
+++ b/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown
@ -146,9 +146,6 @@ mark the rectangle area in mask image with 2-pixel or 3-pixel (probable backgrou
 mark our sure_foreground with 1-pixel as we did in second example. Then directly apply the grabCut
 function with mask mode.

-Additional Resources
--------------------
-
 Exercises
 ---------

--- a/doc/py_tutorials/py_imgproc/py_gradients/py_gradients.markdown
+++ b/doc/py_tutorials/py_imgproc/py_gradients/py_gradients.markdown
@ -103,9 +103,3 @@ plt.show()
 Check the result below:

 ![image](images/double_edge.jpg)
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.markdown
+++ b/doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.markdown
@ -125,9 +125,3 @@ output of that code for the same image as above:

 You can clearly see in the histogram what colors are present, blue is there, yellow is there, and
 some white due to chessboard is there. Nice !!!
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_backprojection/py_histogram_backprojection.markdown
+++ b/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_backprojection/py_histogram_backprojection.markdown
@ -123,6 +123,3 @@ Additional Resources

 -#  "Indexing via color histograms", Swain, Michael J. , Third international conference on computer
    vision,1990.
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown
+++ b/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown
@ -197,6 +197,3 @@ Additional Resources
 --------------------

 -#  [Cambridge in Color website](http://www.cambridgeincolour.com/tutorials/histograms1.htm)
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_equalization/py_histogram_equalization.markdown
+++ b/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_equalization/py_histogram_equalization.markdown
@ -151,6 +151,3 @@ Also check these SOF questions regarding contrast adjustment:
    C?](http://stackoverflow.com/questions/10549245/how-can-i-adjust-contrast-in-opencv-in-c)
 4.  [How do I equalize contrast & brightness of images using
    opencv?](http://stackoverflow.com/questions/10561222/how-do-i-equalize-contrast-brightness-of-images-using-opencv)
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.markdown
+++ b/doc/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.markdown
@ -45,9 +45,3 @@ cv.destroyAllWindows()
 Result is shown below:

 ![image](images/houghcircles2.jpg)
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown
+++ b/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown
@ -103,6 +103,3 @@ Additional Resources
 --------------------

 -#  [Hough Transform on Wikipedia](http://en.wikipedia.org/wiki/Hough_transform)
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.markdown
+++ b/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.markdown
@ -152,6 +152,3 @@ Additional Resources
 --------------------

 -#  [Morphological Operations](http://homepages.inf.ed.ac.uk/rbf/HIPR2/morops.htm) at HIPR2
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_pyramids/py_pyramids.markdown
+++ b/doc/py_tutorials/py_imgproc/py_pyramids/py_pyramids.markdown
@ -139,6 +139,3 @@ Additional Resources
 --------------------

 -#  [Image Blending](http://pages.cs.wisc.edu/~csverma/CS766_09/ImageMosaic/imagemosaic.html)
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_template_matching/py_template_matching.markdown
+++ b/doc/py_tutorials/py_imgproc/py_template_matching/py_template_matching.markdown
@ -132,9 +132,3 @@ cv.imwrite('res.png',img_rgb)
 Result:

 ![image](images/res_mario.jpg)
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.markdown
+++ b/doc/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.markdown
@ -291,6 +291,3 @@ Additional Resources
    Theory](http://cns-alumni.bu.edu/~slehar/fourier/fourier.html) by Steven Lehar
 2.  [Fourier Transform](http://homepages.inf.ed.ac.uk/rbf/HIPR2/fourier.htm) at HIPR
 3.  [What does frequency domain denote in case of images?](http://dsp.stackexchange.com/q/1637/818)
-
-Exercises
---------
--- a/doc/py_tutorials/py_ml/py_kmeans/py_kmeans_opencv/py_kmeans_opencv.markdown
+++ b/doc/py_tutorials/py_ml/py_kmeans/py_kmeans_opencv/py_kmeans_opencv.markdown
@ -186,9 +186,3 @@ cv.destroyAllWindows()
 See the result below for K=8:

 ![image](images/oc_color_quantization.jpg)
-
-Additional Resources
--------------------
-
-Exercises
---------
--- a/doc/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.markdown
+++ b/doc/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.markdown
@ -80,6 +80,3 @@ Additional Resources

 -#  [Machine Learning Course](https://www.coursera.org/course/ml), Video lectures by Prof. Andrew Ng
    (Some of the images are taken from this)
-
-Exercises
---------
--- a/doc/py_tutorials/py_ml/py_svm/py_svm_basics/py_svm_basics.markdown
+++ b/doc/py_tutorials/py_ml/py_svm/py_svm_basics/py_svm_basics.markdown
@ -130,5 +130,3 @@ Additional Resources

 -#  [NPTEL notes on Statistical Pattern Recognition, Chapters
    25-29](https://nptel.ac.in/courses/117108048)
-Exercises
---------
--- a/doc/py_tutorials/py_photo/py_non_local_means/py_non_local_means.markdown
+++ b/doc/py_tutorials/py_photo/py_non_local_means/py_non_local_means.markdown
@ -147,6 +147,3 @@ Additional Resources
    recommended to visit. Our test image is generated from this link)
 2.  [Online course at coursera](https://www.coursera.org/course/images) (First image taken from
    here)
-
-Exercises
---------
--- a/doc/py_tutorials/py_setup/py_setup_in_fedora/py_setup_in_fedora.markdown
+++ b/doc/py_tutorials/py_setup/py_setup_in_fedora/py_setup_in_fedora.markdown
@ -237,9 +237,6 @@ make doxygen
@endcode
 Then open opencv/build/doc/doxygen/html/index.html and bookmark it in the browser.

-Additional Resources
--------------------
-
 Exercises
 ---------

--- a/doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.markdown
+++ b/doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.markdown
@ -116,9 +116,6 @@ Building OpenCV from source
@note We have installed with no other support like TBB, Eigen, Qt, Documentation etc. It would be
 difficult to explain it here. A more detailed video will be added soon or you can just hack around.

-Additional Resources
--------------------
-
 Exercises
 ---------