OSSForks/opencv
1
0
Fork 0
mirror of https://github.com/zebrajr/opencv.git synced 2025-12-06 12:19:50 +01:00
Code Issues Actions Packages Projects Releases Wiki Activity

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

Browse Source 
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.
This commit is contained in:
Nishanth 2024-08-09 03:34:44 -04:00 committed by GitHub
parent c9df679943
commit 6cd730a02c
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
42 changed files with 0 additions and 174 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown
View File

@ -216,8 +216,6 @@ for i in range(len(objpoints)):
print( "total error: {}".format(mean_error/len(objpoints)) ) print( "total error: {}".format(mean_error/len(objpoints)) )
@endcode @endcode
Additional Resources
--------------------
Exercises Exercises
--------- ---------

3
doc/py_tutorials/py_calib3d/py_epipolar_geometry/py_epipolar_geometry.markdown
View File

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

6
doc/py_tutorials/py_calib3d/py_pose/py_pose.markdown
View File

@ -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 If you are interested in graphics, augmented reality etc, you can use OpenGL to render more
complicated figures. complicated figures.
Additional Resources
--------------------
Exercises
---------

6
doc/py_tutorials/py_core/py_basic_ops/py_basic_ops.markdown
View File

@ -195,9 +195,3 @@ See the result below. (Image is displayed with matplotlib. So RED and BLUE chann
interchanged): interchanged):
![image](images/border.jpg) ![image](images/border.jpg)
Additional Resources
--------------------
Exercises
---------

3
doc/py_tutorials/py_core/py_image_arithmetics/py_image_arithmetics.markdown
View File

@ -110,9 +110,6 @@ img2_fg.
![image](images/overlay.jpg) ![image](images/overlay.jpg)
Additional Resources
--------------------
Exercises Exercises
--------- ---------

3
doc/py_tutorials/py_core/py_optimization/py_optimization.markdown
View File

@ -163,6 +163,3 @@ Additional Resources
2. Scipy Lecture Notes - [Advanced 2. Scipy Lecture Notes - [Advanced
Numpy](http://scipy-lectures.github.io/advanced/advanced_numpy/index.html#advanced-numpy) 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/) 3. [Timing and Profiling in IPython](http://pynash.org/2013/03/06/timing-and-profiling/)
Exercises
---------

3
doc/py_tutorials/py_feature2d/py_fast/py_fast.markdown
View File

@ -138,6 +138,3 @@ Additional Resources
2. Edward Rosten, Reid Porter, and Tom Drummond, "Faster and better: a machine learning approach to 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. corner detection" in IEEE Trans. Pattern Analysis and Machine Intelligence, 2010, vol 32, pp.
105-119. 105-119.
Exercises
---------

6
doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown
View File

@ -102,9 +102,3 @@ plt.imshow(img3, 'gray'),plt.show()
See the result below. Object is marked in white color in cluttered image: See the result below. Object is marked in white color in cluttered image:
![image](images/homography_findobj.jpg) ![image](images/homography_findobj.jpg)
Additional Resources
--------------------
Exercises
---------

6
doc/py_tutorials/py_feature2d/py_features_meaning/py_features_meaning.markdown
View File

@ -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, So in this module, we are looking to different algorithms in OpenCV to find features, describe them,
match them etc. match them etc.
Additional Resources
--------------------
Exercises
---------

6
doc/py_tutorials/py_feature2d/py_matcher/py_matcher.markdown
View File

@ -209,9 +209,3 @@ plt.imshow(img3,),plt.show()
See the result below: See the result below:
![image](images/matcher_flann.jpg) ![image](images/matcher_flann.jpg)
Additional Resources
--------------------
Exercises
---------

3
doc/py_tutorials/py_feature2d/py_orb/py_orb.markdown
View File

@ -93,6 +93,3 @@ Additional Resources
-# Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary R. Bradski: ORB: An efficient alternative to -# Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary R. Bradski: ORB: An efficient alternative to
SIFT or SURF. ICCV 2011: 2564-2571. SIFT or SURF. ICCV 2011: 2564-2571.
Exercises
---------

6
doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
View File

@ -67,9 +67,3 @@ See the result below:
![image](images/shitomasi_block1.jpg) ![image](images/shitomasi_block1.jpg)
This function is more appropriate for tracking. We will see that when its time comes. This function is more appropriate for tracking. We will see that when its time comes.
Additional Resources
--------------------
Exercises
---------

6
doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
View File

@ -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. 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. That we will learn in coming chapters.
Additional Resources
--------------------
Exercises
---------

6
doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown
View File

@ -155,9 +155,3 @@ Finally we check the descriptor size and change it to 128 if it is only 64-dim.
(47, 128) (47, 128)
@endcode @endcode
Remaining part is matching which we will do in another chapter. Remaining part is matching which we will do in another chapter.
Additional Resources
--------------------
Exercises
---------

2
doc/py_tutorials/py_gui/py_mouse_handling/py_mouse_handling.markdown
View File

@ -101,8 +101,6 @@ while(1):
cv.destroyAllWindows() cv.destroyAllWindows()
@endcode @endcode
Additional Resources
--------------------
Exercises Exercises
--------- ---------

6
doc/py_tutorials/py_gui/py_video_display/py_video_display.markdown
View File

@ -152,9 +152,3 @@ cap.release()
out.release() out.release()
cv.destroyAllWindows() cv.destroyAllWindows()
@endcode @endcode
Additional Resources
--------------------
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.markdown
View File

@ -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 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. these values, but don't forget to adjust the HSV ranges.
Additional Resources
--------------------
Exercises Exercises
--------- ---------

6
doc/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.markdown
View File

@ -199,9 +199,3 @@ righty = int(((cols-x)*vy/vx)+y)
cv.line(img,(cols-1,righty),(0,lefty),(0,255,0),2) cv.line(img,(cols-1,righty),(0,lefty),(0,255,0),2)
@endcode @endcode
![image](images/fitline.jpg) ![image](images/fitline.jpg)
Additional Resources
--------------------
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_contours/py_contour_properties/py_contour_properties.markdown
View File

@ -114,9 +114,6 @@ For eg, if I apply it to an Indian map, I get the following result :
![image](images/extremepoints.jpg) ![image](images/extremepoints.jpg)
Additional Resources
--------------------
Exercises Exercises
--------- ---------

6
doc/py_tutorials/py_imgproc/py_contours/py_contours_begin/py_contours_begin.markdown
View File

@ -88,9 +88,3 @@ the contour array (drawn in blue color). First image shows points I got with cv.
much memory it saves!!! much memory it saves!!!
![image](images/none.jpg) ![image](images/none.jpg)
Additional Resources
--------------------
Exercises
---------

6
doc/py_tutorials/py_imgproc/py_contours/py_contours_hierarchy/py_contours_hierarchy.markdown
View File

@ -212,9 +212,3 @@ array([[[ 7, -1, 1, -1],
[ 8, 0, -1, -1], [ 8, 0, -1, -1],
[-1, 7, -1, -1]]]) [-1, 7, -1, -1]]])
@endcode @endcode
Additional Resources
--------------------
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_contours/py_contours_more_functions/py_contours_more_functions.markdown
View File

@ -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 moments invariant to translation, rotation and scale. Seventh one is skew-invariant. Those values
can be found using **cv.HuMoments()** function. can be found using **cv.HuMoments()** function.
Additional Resources
====================
Exercises Exercises
--------- ---------

3
doc/py_tutorials/py_imgproc/py_filtering/py_filtering.markdown
View File

@ -150,6 +150,3 @@ Additional Resources
-------------------- --------------------
-# Details about the [bilateral filtering](http://people.csail.mit.edu/sparis/bf_course/) -# Details about the [bilateral filtering](http://people.csail.mit.edu/sparis/bf_course/)
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.markdown
View File

@ -163,6 +163,3 @@ Additional Resources
-------------------- --------------------
-# "Computer Vision: Algorithms and Applications", Richard Szeliski -# "Computer Vision: Algorithms and Applications", Richard Szeliski
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown
View File

@ -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 mark our sure_foreground with 1-pixel as we did in second example. Then directly apply the grabCut
function with mask mode. function with mask mode.
Additional Resources
--------------------
Exercises Exercises
--------- ---------

6
doc/py_tutorials/py_imgproc/py_gradients/py_gradients.markdown
View File

@ -103,9 +103,3 @@ plt.show()
Check the result below: Check the result below:
![image](images/double_edge.jpg) ![image](images/double_edge.jpg)
Additional Resources
--------------------
Exercises
---------

6
doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.markdown
View File

@ -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 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 !!! some white due to chessboard is there. Nice !!!
Additional Resources
--------------------
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_histograms/py_histogram_backprojection/py_histogram_backprojection.markdown
View File

@ -123,6 +123,3 @@ Additional Resources
-# "Indexing via color histograms", Swain, Michael J. , Third international conference on computer -# "Indexing via color histograms", Swain, Michael J. , Third international conference on computer
vision,1990. vision,1990.
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.markdown
View File

@ -197,6 +197,3 @@ Additional Resources
-------------------- --------------------
-# [Cambridge in Color website](http://www.cambridgeincolour.com/tutorials/histograms1.htm) -# [Cambridge in Color website](http://www.cambridgeincolour.com/tutorials/histograms1.htm)
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_histograms/py_histogram_equalization/py_histogram_equalization.markdown
View File

@ -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) 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 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) opencv?](http://stackoverflow.com/questions/10561222/how-do-i-equalize-contrast-brightness-of-images-using-opencv)
Exercises
---------

6
doc/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.markdown
View File

@ -45,9 +45,3 @@ cv.destroyAllWindows()
Result is shown below: Result is shown below:
![image](images/houghcircles2.jpg) ![image](images/houghcircles2.jpg)
Additional Resources
--------------------
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown
View File

@ -103,6 +103,3 @@ Additional Resources
-------------------- --------------------
-# [Hough Transform on Wikipedia](http://en.wikipedia.org/wiki/Hough_transform) -# [Hough Transform on Wikipedia](http://en.wikipedia.org/wiki/Hough_transform)
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.markdown
View File

@ -152,6 +152,3 @@ Additional Resources
-------------------- --------------------
-# [Morphological Operations](http://homepages.inf.ed.ac.uk/rbf/HIPR2/morops.htm) at HIPR2 -# [Morphological Operations](http://homepages.inf.ed.ac.uk/rbf/HIPR2/morops.htm) at HIPR2
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_pyramids/py_pyramids.markdown
View File

@ -139,6 +139,3 @@ Additional Resources
-------------------- --------------------
-# [Image Blending](http://pages.cs.wisc.edu/~csverma/CS766_09/ImageMosaic/imagemosaic.html) -# [Image Blending](http://pages.cs.wisc.edu/~csverma/CS766_09/ImageMosaic/imagemosaic.html)
Exercises
---------

6
doc/py_tutorials/py_imgproc/py_template_matching/py_template_matching.markdown
View File

@ -132,9 +132,3 @@ cv.imwrite('res.png',img_rgb)
Result: Result:
![image](images/res_mario.jpg) ![image](images/res_mario.jpg)
Additional Resources
--------------------
Exercises
---------

3
doc/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.markdown
View File

@ -291,6 +291,3 @@ Additional Resources
Theory](http://cns-alumni.bu.edu/~slehar/fourier/fourier.html) by Steven Lehar 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 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) 3. [What does frequency domain denote in case of images?](http://dsp.stackexchange.com/q/1637/818)
Exercises
---------

6
doc/py_tutorials/py_ml/py_kmeans/py_kmeans_opencv/py_kmeans_opencv.markdown
View File

@ -186,9 +186,3 @@ cv.destroyAllWindows()
See the result below for K=8: See the result below for K=8:
![image](images/oc_color_quantization.jpg) ![image](images/oc_color_quantization.jpg)
Additional Resources
--------------------
Exercises
---------

3
doc/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.markdown
View File

@ -80,6 +80,3 @@ Additional Resources
-# [Machine Learning Course](https://www.coursera.org/course/ml), Video lectures by Prof. Andrew Ng -# [Machine Learning Course](https://www.coursera.org/course/ml), Video lectures by Prof. Andrew Ng
(Some of the images are taken from this) (Some of the images are taken from this)
Exercises
---------

2
doc/py_tutorials/py_ml/py_svm/py_svm_basics/py_svm_basics.markdown
View File

@ -130,5 +130,3 @@ Additional Resources
-# [NPTEL notes on Statistical Pattern Recognition, Chapters -# [NPTEL notes on Statistical Pattern Recognition, Chapters
25-29](https://nptel.ac.in/courses/117108048) 25-29](https://nptel.ac.in/courses/117108048)
Exercises
---------

3
doc/py_tutorials/py_photo/py_non_local_means/py_non_local_means.markdown
View File

@ -147,6 +147,3 @@ Additional Resources
recommended to visit. Our test image is generated from this link) 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 2. [Online course at coursera](https://www.coursera.org/course/images) (First image taken from
here) here)
Exercises
---------

3
doc/py_tutorials/py_setup/py_setup_in_fedora/py_setup_in_fedora.markdown
View File

@ -237,9 +237,6 @@ make doxygen
@endcode @endcode
Then open opencv/build/doc/doxygen/html/index.html and bookmark it in the browser. Then open opencv/build/doc/doxygen/html/index.html and bookmark it in the browser.
Additional Resources
--------------------
Exercises Exercises
--------- ---------

3
doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.markdown
View File

@ -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 @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. difficult to explain it here. A more detailed video will be added soon or you can just hack around.
Additional Resources
--------------------
Exercises Exercises
--------- ---------