Merge pull request #2120 from mehlukas:3.4-addbgsegm

Move background subtraction python tutorial to proper location (#2120)

* add tutorial from OpenCV-Python Tutorials

* remove trailing whitespace

* remove MOG2 part

* add cross-link to MOG2 tutorial

modules/bgsegm/tutorials/bgsegm_bg_subtraction/bgsegm_bg_subtraction.markdown

+Background Subtraction {#tutorial_bgsegm_bg_subtraction}
+======================
+
+Goal
+----
+
+In this chapter,
+
+-   We will familiarize with the background subtraction methods available in OpenCV.
+
+Basics
+------
+
+Background subtraction is a major preprocessing step in many vision-based applications. For
+example, consider the case of a visitor counter where a static camera takes the number of visitors
+entering or leaving the room, or a traffic camera extracting information about the vehicles etc. In
+all these cases, first you need to extract the person or vehicles alone. Technically, you need to
+extract the moving foreground from static background.
+
+If you have an image of background alone, like an image of the room without visitors, image of the road
+without vehicles etc, it is an easy job. Just subtract the new image from the background. You get
+the foreground objects alone. But in most of the cases, you may not have such an image, so we need
+to extract the background from whatever images we have. It becomes more complicated when there are
+shadows of the vehicles. Since shadows also move, simple subtraction will mark that also as
+foreground. It complicates things.
+
+Several algorithms were introduced for this purpose.
+In the following, we will have a look at two algorithms from the `bgsegm` module.
+
+### BackgroundSubtractorMOG
+
+It is a Gaussian Mixture-based Background/Foreground Segmentation Algorithm. It was introduced in
+the paper "An improved adaptive background mixture model for real-time tracking with shadow
+detection" by P. KadewTraKuPong and R. Bowden in 2001. It uses a method to model each background
+pixel by a mixture of K Gaussian distributions (K = 3 to 5). The weights of the mixture represent
+the time proportions that those colours stay in the scene. The probable background colours are the
+ones which stay longer and more static.
+
+While coding, we need to create a background object using the function,
+**cv.bgsegm.createBackgroundSubtractorMOG()**. It has some optional parameters like length of history,
+number of gaussian mixtures, threshold etc. It is all set to some default values. Then inside the
+video loop, use backgroundsubtractor.apply() method to get the foreground mask.
+
+See a simple example below:
+@code{.py}
+import numpy as np
+import cv2 as cv
+
+cap = cv.VideoCapture('vtest.avi')
+
+fgbg = cv.bgsegm.createBackgroundSubtractorMOG()
+
+while(1):
+    ret, frame = cap.read()
+
+    fgmask = fgbg.apply(frame)
+
+    cv.imshow('frame',fgmask)
+    k = cv.waitKey(30) & 0xff
+    if k == 27:
+        break
+
+cap.release()
+cv.destroyAllWindows()
+@endcode
+( All the results are shown at the end for comparison).
+
+@note Documentation on the newer method **cv.createBackgroundSubtractorMOG2()** can be found here: @ref tutorial_background_subtraction
+
+### BackgroundSubtractorGMG
+
+This algorithm combines statistical background image estimation and per-pixel Bayesian segmentation.
+It was introduced by Andrew B. Godbehere, Akihiro Matsukawa, and Ken Goldberg in their paper "Visual
+Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive Audio Art
+Installation" in 2012. As per the paper, the system ran a successful interactive audio art
+installation called “Are We There Yet?” from March 31 - July 31 2011 at the Contemporary Jewish
+Museum in San Francisco, California.
+
+It uses first few (120 by default) frames for background modelling. It employs probabilistic
+foreground segmentation algorithm that identifies possible foreground objects using Bayesian
+inference. The estimates are adaptive; newer observations are more heavily weighted than old
+observations to accommodate variable illumination. Several morphological filtering operations like
+closing and opening are done to remove unwanted noise. You will get a black window during first few
+frames.
+
+It would be better to apply morphological opening to the result to remove the noises.
+@code{.py}
+import numpy as np
+import cv2 as cv
+
+cap = cv.VideoCapture('vtest.avi')
+
+kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE,(3,3))
+fgbg = cv.bgsegm.createBackgroundSubtractorGMG()
+
+while(1):
+    ret, frame = cap.read()
+
+    fgmask = fgbg.apply(frame)
+    fgmask = cv.morphologyEx(fgmask, cv.MORPH_OPEN, kernel)
+
+    cv.imshow('frame',fgmask)
+    k = cv.waitKey(30) & 0xff
+    if k == 27:
+        break
+
+cap.release()
+cv.destroyAllWindows()
+@endcode
+Results
+-------
+
+**Original Frame**
+
+Below image shows the 200th frame of a video
+
+![image](images/resframe.jpg)
+
+**Result of BackgroundSubtractorMOG**
+
+![image](images/resmog.jpg)
+
+**Result of BackgroundSubtractorGMG**
+
+Noise is removed with morphological opening.
+
+![image](images/resgmg.jpg)
+
+Additional Resources
+--------------------
+
+Exercises
+---------

modules/bgsegm/tutorials/table_of_content_bgsegm.markdown

+Tutorials for bgsegm module {#tutorial_table_of_content_bgsegm}
+===============================================================
+
+-   @subpage tutorial_bgsegm_bg_subtraction
+
+    In several applications, we need to extract foreground for further operations like object tracking. Background Subtraction is a well-known method in those cases.