update doc

Author: BenNG

.gitignore

@@ -4,4 +4,6 @@ mnist
 build/
 temp.temp
 .vscode
-Testing
+Testing
+test
+.history

README.md

@@ -1,56 +1,64 @@
 # Sudoku-recognizer
 
 ## Requirement
-* CMake [[sources](https://cmake.org/)]  
-* Boost v.1.58.0 (test only) [[sources]](http://www.boost.org/users/history/version_1_58_0.html)
-  * ./bootstrap --with-libraries=test
-  * ./b2
-  * sudo ./b2 install
-* OpenCV 3.1.0 [[sources](http://opencv.org/downloads.html)] [[more info](http://docs.opencv.org/3.1.0/df/d65/tutorial_table_of_content_introduction.html)]
+
+- CMake [[sources](https://cmake.org/)]
+- Boost v.1.58.0 (test only) [[sources]](http://www.boost.org/users/history/version_1_58_0.html)
+  - ./bootstrap.sh --with-libraries=test
+  - ./b2
+  - sudo ./b2 install
+- OpenCV 3.1.0 [[install](https://docs.opencv.org/3.1.0/d7/d9f/tutorial_linux_install.html)] [[more info](http://docs.opencv.org/3.1.0/df/d65/tutorial_table_of_content_introduction.html)]
   - build for desktop application
     - Download, extract and get yourself inside the sources of OpenCV 3.1.0 and create a `build` folder. Then:
     ```
     cd build
     # CMAKE_BUILD_TYPE could either "Release" or "Debug".
     # WITH_OPENCL The usage of OpenCL is a big improvement in terms of performance but you don't have to worry about it if you system has support you got it for free. On ubuntu I had to install
-    sudo apt-get install opencl-headers beignet beignet-dev  
-    cmake -DCMAKE_BUILD_TYPE=Release -DWITH_OPENCL=ON ..
+    sudo apt-get install opencl-headers beignet beignet-dev
+    cmake -DCMAKE_BUILD_TYPE=Debug -DWITH_OPENCL=ON -DENABLE_PRECOMPILED_HEADERS=OFF ..
     make -j4
     sudo make install
     ```
   - build for android application [here](http://answers.opencv.org/question/126795/build-opencv-31-for-android-with-opencl-support/)
 
-
 ## Useful information
+
 Note that all the commands have to be invoked in the `build` folder. The first time you have to create it.
+
 ```
 mkdir build && cd build
 ```
 
 ## Recognizer
-![hhh.gif](https://bitbucket.org/repo/zpnRyB/images/276570042-sudoku4.gif)
 
+![hhh.gif](https://bitbucket.org/repo/zpnRyB/images/276570042-sudoku4.gif)
 
 ### Tests
+
 Once you have installed all the requirements, execute the tests to see if everything is OK
+
 ```
-cmake .. && ctest -V
+cmake .. && make &&  ctest -V
 ```
 
 ### Parse one of the provided puzzle located in `assets/puzzles`
+
 ```
 cmake .. && make && src/recognize ./../assets/puzzles/s1.jpg
 ```
 
 ### Tools that can help you during the development process
+
 #### Show all extracted puzzles located in `assets/puzzles`
+
 ```
 cmake .. && make && src/showExtracted
 ```
 
 ![eee.gif](https://bitbucket.org/repo/njp6xM/images/4118982265-eee.gif)
 
 #### Show all extracted cells for `assets/puzzles/s33.jpg`
+
 ```
 cmake .. && make && src/showExtracted --puzzleNumber 33 --showCell
 ```
@@ -59,42 +67,50 @@ cmake .. && make && src/showExtracted --puzzleNumber 33 --showCell
 
 #### Other actions
 
-##### will show all extracted puzzles  
+##### will show all extracted puzzles
+
 ```
-cmake .. && make && src/showExtracted  
+cmake .. && make && src/showExtracted
 ```
 
-##### will show all extracted cells  
+##### will show all extracted cells
+
 ```
-cmake .. && make && src/showExtracted --showCell  
+cmake .. && make && src/showExtracted --showCell
 ```
 
-##### will show the extracted puzzle: `assets/puzzles/s33.jpg`  
+##### will show the extracted puzzle: `assets/puzzles/s33.jpg`
+
 ```
-cmake .. && make && src/showExtracted --puzzleNumber 33  
+cmake .. && make && src/showExtracted --puzzleNumber 33
 ```
 
-##### will show the cell index: 10 of `assets/puzzles/s33.jpg`  
+##### will show the cell index: 10 of `assets/puzzles/s33.jpg`
+
 ```
 ## useful in situation where only cell number 40 is wrong
-cmake .. && make && src/showExtracted --puzzleNumber 33 --showCell --cellNumber 10  
+cmake .. && make && src/showExtracted --puzzleNumber 33 --showCell --cellNumber 10
 ```
 
-##### will show the cell index: 10 of `assets/puzzles/s33.jpg` plus the process of extraction  
+##### will show the cell index: 10 of `assets/puzzles/s33.jpg` plus the process of extraction
+
 ```
-cmake .. && make && src/showExtracted --puzzleNumber 33 --showCell --cellNumber 10 --debug  
+cmake .. && make && src/showExtracted --puzzleNumber 33 --showCell --cellNumber 10 --debug
 ```
 
 ### Create the `assets/raw-features.yml` yourself
+
 ```
 cmake .. && make && src/prepareData # create training data from s0 --> s35
 ```
 
-* The knn network can be tested using this:
+- The knn network can be tested using this:
+
 ```
 cmake .. && make && src/testKnn
 ```
-> You can iterate over each picture by setting debug=true in the `testKnn` fn  
+
+> You can iterate over each picture by setting debug=true in the `testKnn` fn
 
 ## The whole process
 
@@ -104,43 +120,45 @@ cmake .. && make && src/mouline ./../assets/puzzles/s0.jpg
 
 ![iii.gif](https://bitbucket.org/repo/njp6xM/images/1534426116-iii.gif)
 
-All the hard work was to recognize the numbers that's why this part comes after the `Recognizer` even if obviously this is what people want to do. 
+All the hard work was to recognize the numbers that's why this part comes after the `Recognizer` even if obviously this is what people want to do.
 A [mouline](https://github.com/BenNG/sudoku-recognizer/blob/master/src/lib/sudoku.cpp#L1743) function has been created that gather all other functions.
 So the process is:
- * parse the original image that holds the sudoku puzzle and extract it
- * parse the extracted puzzle and use a computer vision algorithm to grab the initial state.
- * Solve the sudoku
- * write the solution on the extracted puzzle
- * merge the extracted puzzle on the original picture
+
+- parse the original image that holds the sudoku puzzle and extract it
+- parse the extracted puzzle and use a computer vision algorithm to grab the initial state.
+- Solve the sudoku
+- write the solution on the extracted puzzle
+- merge the extracted puzzle on the original picture
 
 ## Tips (Personal memo)
-* There are 2 extraction process
-  * the extraction of the puzzle
-  * the extraction of the grid within the puzzle (somethimes it is the same see s6.jpg)
-* If you want to play with `HOGDescriptor` add set(OpenCV_LIBS opencv_core opencv_objdetect) (this is not used in the project anymore)
-* The function `prepareData` creates `assets/raw-features.yml` and getKnn uses it
-* boost library (used only for test)
- * check is the /FindBoost.cmake the supported version (the lib is not used anymore because it is not possible to use it on android)
-* If you ask for a cell and you have an error chances are that your cell is a 1 and it is too tiny go to extractNumber and play with (and do not forget to run prepareData && test after)
-  * area
-  * boundingArea
-  * width_threshold
-  * height_threshold
-* Before the portage on android, I used a system for resolving the path of file that looked for the root of the project and join the path given but in a apk application
-  there are no path so I had to rewrite all and use relative paths
-* adding curl to the project (not use anymore)
-  * dl the source code [here](https://github.com/curl/curl/releases)
-  * in the source code there is a `CMakeLists.txt` so:
-    * cmake .
-    * make
-    * sudo make install
-  * [then](https://github.com/BenNG/sudoku-recognizer/blob/2f30e4dc54620b646df8a97a8ec5651a171c3e56/src/CMakeLists.txt#L1)
-  * [and](https://github.com/BenNG/sudoku-recognizer/blob/2f30e4dc54620b646df8a97a8ec5651a171c3e56/src/CMakeLists.txt#L31)
-* When you do a project using computer vision think about the resolution you will use for:
-  * the camera
-  * the image being processed by the algorithm   
 
+- There are 2 extraction process
+  - the extraction of the puzzle
+  - the extraction of the grid within the puzzle (somethimes it is the same see s6.jpg)
+- If you want to play with `HOGDescriptor` add set(OpenCV_LIBS opencv_core opencv_objdetect) (this is not used in the project anymore)
+- The function `prepareData` creates `assets/raw-features.yml` and getKnn uses it
+- boost library (used only for test)
+- check is the /FindBoost.cmake the supported version (the lib is not used anymore because it is not possible to use it on android)
+- If you ask for a cell and you have an error chances are that your cell is a 1 and it is too tiny go to extractNumber and play with (and do not forget to run prepareData && test after)
+  - area
+  - boundingArea
+  - width_threshold
+  - height_threshold
+- Before the portage on android, I used a system for resolving the path of file that looked for the root of the project and join the path given but in a apk application
+  there are no path so I had to rewrite all and use relative paths
+- adding curl to the project (not use anymore)
+  - dl the source code [here](https://github.com/curl/curl/releases)
+  - in the source code there is a `CMakeLists.txt` so:
+    - cmake .
+    - make
+    - sudo make install
+  - [then](https://github.com/BenNG/sudoku-recognizer/blob/2f30e4dc54620b646df8a97a8ec5651a171c3e56/src/CMakeLists.txt#L1)
+  - [and](https://github.com/BenNG/sudoku-recognizer/blob/2f30e4dc54620b646df8a97a8ec5651a171c3e56/src/CMakeLists.txt#L31)
+- When you do a project using computer vision think about the resolution you will use for:
+  - the camera
+  - the image being processed by the algorithm
 
 ## Thank you for answering my questions
-* [Berak](http://answers.opencv.org/users/2130/berak/)
-* [Baptiste Wicht](https://github.com/wichtounet/)
+
+- [Berak](http://answers.opencv.org/users/2130/berak/)
+- [Baptiste Wicht](https://github.com/wichtounet/)