Remove annoying trailing white space by c++

  If you ever try to commit something to opencv(I am porting/implementing various image hash algorithms to opencv_contrib when I writing this post, you can find my branch at here), you would likely to find out some extremely annoying messages as

modules/tracking/include/opencv2/tracking/tracker.hpp:857: trailing whitespace.
+  
modules/tracking/include/opencv2/tracking/tracker.hpp:880: trailing whitespace.
+ 
modules/tracking/include/opencv2/tracking/tracker.hpp:890: trailing whitespace.
+ 
modules/tracking/include/opencv2/tracking/tracker.hpp:1433: trailing whitespace.
+        Params(); 
modules/tracking/include/opencv2/tracking/tracker.hpp:1434: trailing whitespace.
+        
modules/tracking/include/opencv2/tracking/tracker.hpp:1444: trailing whitespace.

blablabla. They pop out in your files time to time, cost you more times to fix them, pollute your commit history, not only that, those trailing white spaces, they are hard to spot by human eyes.

Apparently, eliminate those trailing white space is not a job suit for humans, we would better leave those tedious tasks to our friends--computer.

    To teach our friend know what do I want to do, I write a small program to help us(source codes located at here), you should be able to compile and run it if you familiar with c++ and boost.

    Enough of talk, let me show you an example

Example 00

    As you can see, Example 00 contains a lot of tabs and trailing white space, not only that, there are a tab we should not removed(tab of std::string("\t")), this is the time my small tool--kill_trailing_white_space come in. All you need to do is specify you want to remove the tab and trailing white space of a file, or the files inside the folder(will scan the folders recursively). Example

"kill_trailing_white_space --input_file main.cpp"

"kill_trailing_white_space --input_folder img_hash"

    After the process, we could have a clean file as Example 01.

Example 01

    You can see the help menu if you enter --help.By now this small tool only support the files with extension ".hpp" and ".cpp". Feel free to modify the codes to suit your needs.

Content based image retrieval(CBIR) 00--Use CBIR to find similar images of ukbench

    Content based image retrieval(CBIR), also called as query by image content(QBIC), google search by image and TinEye maybe are the famous example in our daily live. In short, CBIR search the images based on the content of the image, not the name,date,meta data or other info.

    I study how to implement CBIR from PyImageSearch Gurus, the algorithms are almost the same, but my codes are written by c++, build on top of opencv, hdf5, armadillo, boost, rapidjson. I pick c++ but not python(PyImageSearch use python) for this project because

1 : c++ suit for building stand alone package
2 : I like c++

    These series of post would not discuss the implementation details of the codes(codes located at github) but summarize the keys I learn from the CBIR lessons of PyImageSearch Gurus.

    The keys of this CBIR system

1 : Feature detector--kaze
2 : Feature descriptor--kaze
3 : Bag of visual words
4 : Data structure(hdf5, inverted index)
5 : Create Code book(I prefer kmeans)
6 : Quantization(build a histogram)
7 : Tf-idf(Term frequency and inverse document frequency)
8 : Spatial verification
9 : Evaluation

   ukbench contain 6376 images, it would be a tedious job to find relevant images by human, this is why we need CBIR to save us from this kind of labor.  Before I begin to summarize the keys of this CBIR system, I would post some examples, a picture is worth a thousand words.

Case 1 : Find similar image of the camera(pic00) within ukbench

pic00

Search result of pic00

Case 2 : Find similar image of the toy(pic01) within ukbench

 

pic01

Search result of pic01

    The most similar image are shown at the first row. Until now, I think it should be clear enough to show what are CBIR intent to solve. We could use it to deal with a lot of problems, like object recognition(however, cnn is state of the art when I writing this post), search web site with the image(like google and TinEye), remove duplicate images and so on.

    On next post, I would record part of the flows of this CBIR system, write down how to use the codes located on github(without explanation of  implementation details).