/**
 * Course material for the "Konenako 2010" course.
 */

/**
 * Abstract base class for a dumb grayscale image feature
 * extractor. Why is it dumb? Because it only deals with grayscale
 * images which must be encoded as row-major vectors of double
 * numbers. This was the fastest route for me to prepare exercises for
 * the hands-on lecture on 2010-11-18. In reality, you would use some
 * image processing library such as ImageJ.
 * 
 * How to use: You derive a concrete SomeExtractor and implement the
 * method extractFromGrayscale() which gets a height-by-width double
 * array as argument and is expected to return a double vector
 * containing the nice features extracted by your code.
 * 
 * @author nieminen
 *
 */
public abstract class AbstractDumbGrayscaleFeatureExtractor 
         implements FeatureExtractor{
    protected int height;
    protected int width;
    
    public AbstractDumbGrayscaleFeatureExtractor(){
        throw new RuntimeException("Must not instantiate without height and width.");
    }
    public AbstractDumbGrayscaleFeatureExtractor(int height,int width) {
        this.height=height;
        this.width=width;
    }

    /**
     * Extracts a feature from a raw vector. In this case we assume that it is 
     * a grayscale image encoded as a row-major 1d array (concatenated rows).
     * 
     * @param input
     * @return
     */
    public double[] extractFromRawVector(double[] input){
        double[][] image = new double[height][width];
        for(int row=0;row<height;row++){
            for(int col=0;col<width;col++){
                image[row][col] = input[row*width+col];
            }
        }
        return extractFromGrayscale(image);
    }
    abstract double[] extractFromGrayscale(double[][] image);
}