Transcript Descargar - PEDRO EVARISTO GS

Sudoku Downloader and
Recognizer
Author: Pedro Evaristo González Sánchez
Main Activity(Portrait)
Custom view
- Paint and Canvas to draw,
background, lines and numbers
- Adaptable ( OnSizeChanged )
- Events
Self-management(
onTouchEvent, onKeypressed )
- Interaction with Main Activity
Custom View – Main Activity
Class Sudoku (I)
 Matrix [9][9] of Integers
 Constructor overload ( String ) to implement the
communication between activities
 The class return a String which contains the sudoku board.
This is very helpful for several situations. For innstance (
Bundle and Activities communication)
 Neighbor checking ( row, column and block )
Sudoku Class II ( Backtracking )
- If the actual Cell is (9,9) => Ends
- If you find a number, go to the next cell ( Original Sudoku )
- For each value 1..9
- If neighbor checking is positive
- Sudoku(i+1,j+1)
- If you don’t find a candidate, restore and go back one cell
Download Activity (I)
- A way to update the app game
- It exist a Sudoku board holded in a web server
- “Good formed” Board within a *.txt file.
- The goal is to rescue this board and take it to the Game
Download Activity (II)
3. Sudoku Recognizer
 3.0 Android previous concepts
 3.1 Image Capture
 3.2 Image processing (OpenCV)
 3.3 Optical Chracter Recognition (OCR)
 Tesseract
3.0 Android previous concepts
 SDK and OpenCV Library (OpenCV)
 NDK (Native Developing Kit) ( Tesseract )
 Build libraries and reference them in the application.
3.1 Image Capture
- Calling the Intent in charge of capturing
images of Android
- Capture and save the image in a temporal
file
- Pre-processing => Finding the balance
between good qualitiy and computability
3.2 Image Processing (I)
 First controversy, the image is in perspective => the board is
not a quad, is a trapezoid
 The Main concern is a processing in two levels (RGB and
GRAY)
Image Processing (II)
 Border’s detection
 Next, searching the biggest contour within the image. (ROI)
Image Processing (III)
 Hough algorithm and transform to find the boards lines
 Two fundamental concepts in line processing:
 Every single line will be delete painting them of white or black in
each case (RGB and GRAY).
 Check if the start point or end point of line is inside some of the
four rect corners of the image
Image Processing (IV)
 Getting the matrix that defines the perspective transformation
needed to extrapolate the corners of the board to the corners
of the image
 Apply a WarpPerspective Transformation with the matriz
obtained.
Image Processing (V)
 At this point, the board is divided in equal cells to process
each one of them
Procesamiento de la Imagen (VI)
 Single cell processing in paralell (RGB and Gray) bearing in
mind Tesseracts requeriments.
Equalizing
Histogram
Threshold
Smooth
+
Dilate
findContours()
Tesseract ( OCR )
Tesseract
6
 ¡Don’t wait for a miracle!. You need an strong image
processing
 Delimiting dictionary to numbers (1..9)
 For each number it is necessary releasing the memory
allocated by the image and by the tesseracts object. We are
on a phone and we’ve got less memory resources.
That’s all. Thank you!!