Merge pull request #14614 from mehlukas:3.4-movevideo

doc/py_tutorials/py_tutorials.markdown

@@ -25,7 +25,7 @@ OpenCV-Python Tutorials {#tutorial_py_root}
     In this section
     you will learn about feature detectors and descriptors
 
--   @subpage tutorial_py_table_of_contents_video
+-   @ref tutorial_table_of_content_video
 
     In this section you
     will learn different techniques to work with videos like object tracking etc.

doc/py_tutorials/py_video/py_bg_subtraction/py_bg_subtraction.markdown

@@ -1,173 +1,4 @@
 Background Subtraction {#tutorial_py_bg_subtraction}
 ======================
 
-Goal
+Tutorial content has been moved: @ref tutorial_background_subtraction
-----
-
-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. OpenCV has implemented three such algorithms
-which are very easy to use. We will see them one-by-one.
-
-### 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.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).
-
-### BackgroundSubtractorMOG2
-
-It is also a Gaussian Mixture-based Background/Foreground Segmentation Algorithm. It is based on two
-papers by Z.Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction" in 2004
-and "Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction"
-in 2006. One important feature of this algorithm is that it selects the appropriate number of
-gaussian distribution for each pixel. (Remember, in last case, we took a K gaussian distributions
-throughout the algorithm). It provides better adaptability to varying scenes due illumination
-changes etc.
-
-As in previous case, we have to create a background subtractor object. Here, you have an option of
-detecting shadows or not. If detectShadows = True (which is so by default), it
-detects and marks shadows, but decreases the speed. Shadows will be marked in gray color.
-@code{.py}
-import numpy as np
-import cv2 as cv
-
-cap = cv.VideoCapture('vtest.avi')
-
-fgbg = cv.createBackgroundSubtractorMOG2()
-
-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
-(Results given at the end)
-
-### 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 BackgroundSubtractorMOG2**
-
-Gray color region shows shadow region.
-
-![image](images/resmog2.jpg)
-
-**Result of BackgroundSubtractorGMG**
-
-Noise is removed with morphological opening.
-
-![image](images/resgmg.jpg)
-
-Additional Resources
---------------------
-
-Exercises
----------

doc/py_tutorials/py_video/py_table_of_contents_video.markdown

@@ -1,16 +1,4 @@
 Video Analysis {#tutorial_py_table_of_contents_video}
 ==============
 
--   @ref tutorial_meanshift
+Content has been moved: @ref tutorial_table_of_content_video
-
-    We have already seen
-    an example of color-based tracking. It is simpler. This time, we see significantly better
-    algorithms like "Meanshift", and its upgraded version, "Camshift" to find and track them.
-
--   @ref tutorial_optical_flow
-
-    Now let's discuss an important concept, "Optical Flow", which is related to videos and has many applications.
-
--   @subpage tutorial_py_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.