def GAP(source,Encode=True):    # GAP Prediction
            # Create array of output image
            GAP_out = np.zeros(source.shape);
            dy = source.shape[0];
            dx = source.shape[1];
            for j in range(2,dy):
                for i in range(2,dx-1):
                    In   = int(source[j-1,i]);
                    Inn  = int(source[j-2,i]);
                    Ine  = int(source[j-1,i+1]);
                    Inne = int(source[j-2,i+1]);
                    Inw  = int(source[i-1,j-1]);
                    Iw   = int(source[j,i-1]);
                    Iww  = int(source[j,i-2]);


                    # input to GAP
                    dh = (abso(Iw,Iww) + abso(In,Inw) + abso(In,Ine));
                    dv = (abso(Iw,Inw) + abso(In,Inn) + abso(Ine,Inne));

                    fc = dh-dv;
                    sc = dv-dh;

                    # GAP Algorithm
                    if(sc > 80):
                        x = Iw;
                    elif(fc > 80):
                        x = In;
                    else:
                        x = (In+Iw)/2+(abso(Ine,Inw))/4;
                        if(fc > 32):
                            x = (x+In)/2;
                        elif(sc > 32):
                            x = (x+Iw)/2;
                        elif(fc > 8):
                            x = (3*x+In)/4;
                        elif(sc > 8):
                            x = (3*x+Iw)/4;
                    # End to GAP Algorithm
                    if(Encode):     # Coding
                        GAP_out[j,i] = np.uint8(x)
                        print "Posicion = (",j,",",i,")", x,GAP_out[j,i].astype(np.uint8), source[j,i];
                    else:           # Decoding
                        GAP_out[j,i] = x + error[j,i];