*Fixing typos;
*Making codes more similar to the main one, in C++;
*Adding Grayscale option to the Python and Java codes;
*Fixing python identation, whitespaces and redundancies.
* Extending template_matching tutorial with Java
* adding mask to java version of the tutorial
* adding the python toggle and code
* updating table of content
* adding py and java to table of content
* adding mask to python
* going back to markdown with duplicated text
* non duplicated text
The docstring for one of the Python sample programs includes a link to the research paper describing the main algorithm. That link is no longer valid (results in a 404 error) so this update replaces it with another link from the same institution which is currently valid.
* GSoC 2016 - Adding toggle files to be used by tutorials.
Add a toggle option for tutorials.
* adds a button on the HTML tutorial pages to switch between blocks
* the default option is for languages: one can write a block
for C++ and another one for Python without re-writing the tutorial
Add aliases to the doxyfile.
* adding alises to make a link to previous and next tutorial.
* adding alias to specify the toggle options in the tutorials index.
* adding alias to add a youtube video directly from link.
Add a sample tutorial (mat_mask_opertaions) using the developed aliases:
* youtube alias
* previous and next tutorial alias
* buttons
* languages info for tutorial table of content
* code referances with snippets (and associated sample code files)
* Removing the automatic ordering.
Adding specific toggles for cpp, java and python.
Move all the code to the footer / header and Doxyfile.
Updating documentation.
I noticed that I missed the fact that `cimg` is used in the second `imshow()` call. Changed the scope of the second function call to be within the if-statement. Otherwise in cases where have not been detected the second `imshow()` will attempt to use `cimg` which will be empty leading to an error.
In the C++ equivalent of this example a check is made whether the vector (here in Python we have a list) actually has any lines in it that is whether the Hough lines function has managed to find any in the given image. This check is missing for the Python example and if no lines are found the application breaks.
In the C++ equivalent of this example a check is made whether the vector (here in Python we have a list) actually has any circles in it that is whether the Hough circles function has managed to find any in the given image. This check is missing for the Python example and if no circles are found the application breaks.
1. Following condition is True on each iteration becuase -1 % 0xFF is 255 not -1
code = cv2.waitKey(100) % 0x100
if code != -1:
break
this were resetting point position on each cycle not on key press as intended
2. Previous small bug were masking serious bug with matrix operation on matrices of incorrect size.
As the result on 2nd iteration of internal cycle program has crushed.
I have fixed it too, matrix operation was taken from examples/cpp/kalman.cpp where it looks like
randn( processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0))));
which is something totally different from previous code here.
Example behave as it should now, i.e. point moving by circle trajectory as in C++ example.
Added a sample file for qrcode detection in python
* sample python file for qrcode detection added in samples/python
* input taken using argparse and the indents were removed
* Removed unused variables
* updated the format and removed unused variables
Removed the use of global variables and used parameterised contructor instead
=set multi detection true by default
* added detection from camera
* samples(python): coding style in qrcode.py
