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3D rendering engine. Plus modelling. Expected glsl textures 3d and 2d rendering3D primitives, and a lot of scenes' samples to test.+ Game Jogl reworked, Calculator (numbers and vectors). Java code parser implementation starts (<=1.2)

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/*
%SNAKEMINIMIZE   Minimizes the energy function of a snake
%
% SYNOPSIS:
%  snake_out = snakeminimize(snake_in,Fext,alpha,beta,stepsz,kappa,iterations)
%
% PARAMETERS:
%  snake_in :   initial snake, object of type DIP_SNAKE
%  Fext :       external force field, 2D-2D vector image
%               for example: gradient, gvf, vfc
%  alpha :      elasticity parameter (membrane)
%  beta :       rigidity parameter (thin plate)
%  stepsz :     step size
%  kappa :      balloon force (negative for inwards force)
%  iterations : number of iterations performed
%
% DEFAULTS:
%  alpha = 0.2
%  beta = 0.4
%  stepsz = 1
%  kappa = 0
%  iterations = 20
%
% EXAMPLE:
%  a = noise(50+100*gaussf(rr>85,2),'gaussian',20)
%  f = gradient(gradmag(a,5));
%  f = f./max(norm(f));
%  x = 100+30*cos(0:0.1:2*pi); y = 150+40*sin(0:0.1:2*pi);
%  s = dip_snake([x',y'])
%  s = snakeminimize(s,f,0.01,100,3,0.3,20)
%  s = snakeminimize(s,f,0.01,100,3,0.3,20)
%  s = snakeminimize(s,f,0.01,100,3,0.3,20)
%  s = snakeminimize(s,f,0.01,100,3,0.3,20)
%  s = snakeminimize(s,f,0.01,100,3,0.3,20)
%
% NOTE:
%  This function requires at least MATLAB version 7.6!
%
% LITERATURE:
%  M. Kass, A. Witkin, D. Terzopoulos, "Snakes: Active Contour Models",
%     Int. J. of Computer Vision 4(1):321-331 (1988)
%  L.D. Cohen, I. Cohen, "Finite-element methods for active contour models and
%     balloons for 2-D and 3-D images", IEEE TPAMI 15(11):1131-1147 (1993)
%
% SEE ALSO: dip_snake

% (C) Copyright 2009-2010, All rights reserved.
% Cris Luengo, Uppsala, 18 September 2009.
%
% 4 March 2010: Edited for compatability with DIPimage releases after 2.1.
*/
/*DipSnake snake_in :   initial snake, object of type DIP_SNAKE
M Fext, :       external force field, 2D-2D vector image
%               for example: gradient, gvf, vfc
%  alpha :      elasticity parameter (membrane)
%  beta :       rigidity parameter (thin plate)
%  stepsz :     step size
%  kappa :      balloon force (negative for inwards force)
%  iterations : number of iterations performed
function s = snakeminimize(varargin)
*/
/*        int N = (int) snake_in.size();
        double a = gamma * (2 * alpha + 6 * beta) + 1;
        double b = gamma * (-alpha - 4 * beta);
        double c = gamma * beta;
        M P = M.diag(M.repmat(new double[][]{{a}}, 1, N));
        P = P.plus(M.diag(M.repmat(new double[][]{{b}}, 1, N - 1), 1)).plus(M.diag(new double[][]{{b}}, -N + 1));
        P = P.plus(M.diag(M.repmat(new double[][]{{b}}, 1, N - 1), -1)).plus(M.diag(new double[][]{{b}}, N - 1));
        P = P.plus(M.diag(M.repmat(new double[][]{{c}}, 1, N - 2), 2)).plus(M.diag(new double[][]{{c, c}}, -N + 2));
        P = P.plus(M.diag(M.repmat(new double[][]{{c}}, 1, N - 2), -2)).plus(M.diag(new double[][]{{c, c}}, N - 2));
        P = P.inverse();/*d = struct('menu', 'Segmentation',...
        'display', 'Minimize snake energy',...
        'inparams', struct('name', {'snake_in', 'Fext', 'alpha', 'beta', 'stepsz', 'kappa', 'iterations'},...
        'description', {'Input snake', 'External force', 'Elasticity', 'Rigidity', 'Step size', 'Balloon force', 'Iterations'},...
        'type', {'snake', 'image', 'array', 'array', 'array', 'array', 'array'},...
        'dim_check', {0, 2, 0, 0, 0, 0, 0},...
        'range_check', {[],{
            'real', 'tensor'
        },'R+', 'R+', 'R+', 'R', 'N'},...
        'required', {1, 1, 0, 0, 0, 0, 0},...
        'default', {'dip_snake(x)', 'gradient(gradmag(a))', 0.2, 0.4, 1, 1, 20}...
                            ),...
        'outparams', struct('name', {'snake_out'},...
        'description', {'Output snake'},...
        'type', {'snake'}...
                              )...
          );
          /*
        if nargin == 1
        s = varargin {
            1
        } ;
        if ischar(s) & strcmp(s, 'DIP_GetParamList')
        s = d;
        return
                end
        end
        try
   [s, f, alpha, beta, stepsz, kappa, iterations] =getparams(d, varargin {:});
catch
        if ~isempty(paramerror)
        error(paramerror)
   else
        error(firsterr)
        end
                end

        if prod(imarsize(f)) ~ = 2
        error('The external force image must have 2D vectors ');
        end
                [maxx, maxy] =imsize(f);
        if any(s.imsz >[maxx, maxy])
        error('The snake is defined on an image larger than the given external force image.');
        end
        s.imsz = [maxx, maxy];
        maxx = maxx - 1;
        maxy = maxy - 1;

%Do the snake !
                md = 1; %The average distance we want to keep between points.
                s = resample(s, md);
%h = disp(s);
        P = compute_M(length(s), alpha, beta, stepsz);
        for ii = 1:iterations
                % disp(['iteration ii=', num2str(ii), ', snake length = ', num2str(length(s))])

   %Do we need to resample the snake ?
                d = sqrt(diff(s.x). ^ 2 + diff(s.y). ^ 2);
        if any(d < md / 3) || any(d > md * 3)
                % disp('resampling snake')
        s = resample(s, md);
        P = compute_M(length(s), alpha, beta, stepsz);
        end

                % Calculate external force
        coords = [s.x, s.y];
        fex = get_subpixel(f {
            1
        },coords, 'linear');
        fey = get_subpixel(f {
            2
        },coords, 'linear');

   %Calculate balloon force
        if kappa ~ = 0
        b = [coords(2:end,:);
        coords(1,:)]- [coords(end,:);
        coords(1:end - 1,:)];
        m = sqrt(sum(b. ^ 2, 2));
        bx = b(:,2)./m;
        by = -b(:,1)./m;
      %Add balloon force to external force
        fex = fex + kappa * bx;
        fey = fey + kappa * by;
        end

                % Move control points
        x = P * (s.x + stepsz * fex);
        y = P * (s.y + stepsz * fey);

   %Constrict points to image
        x(x < 0) = 0;
        x(x > maxx) = maxx;
        y(y < 0) = 0;
        y(y > maxy) = maxy;

   %Update snake
        s.x = x;
        s.y = y;

   %if mod(ii, 10)
                % disp(s, h);
   %drawnow;
   %pause(0.5);
   %end

                end


%The M P = (tau * A + I) ^ -1
        function P = compute_M(N, alpha, beta, stepsz)

        a = stepsz * (2 * alpha + 6 * beta) + 1;
        b = stepsz * (-alpha - 4 * beta);
        c = stepsz * beta;
        P = diag(repmat(a, 1, N));
        P = P + diag(repmat(b, 1, N - 1), 1) + diag(b, -N + 1);
        P = P + diag(repmat(b, 1, N - 1), -1) + diag(b, N - 1);
        P = P + diag(repmat(c, 1, N - 2), 2) + diag([c, c],-N + 2);
        P = P + diag(repmat(c, 1, N - 2), -2) + diag([c, c],N - 2);
        P = inv(P);
    }¨*/




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