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## Copyright (C) 2024 David Legland
## All rights reserved.
##
## Redistribution and use in source and binary forms, with or without
## modification, are permitted provided that the following conditions are met:
##
## 1 Redistributions of source code must retain the above copyright notice,
## this list of conditions and the following disclaimer.
## 2 Redistributions in binary form must reproduce the above copyright
## notice, this list of conditions and the following disclaimer in the
## documentation and/or other materials provided with the distribution.
##
## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ''AS IS''
## AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
## IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
## ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE FOR
## ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
## DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
## SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
## CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
## OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
## OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
##
## The views and conclusions contained in the software and documentation are
## those of the authors and should not be interpreted as representing official
## policies, either expressed or implied, of the copyright holders.
function dist = distancePoints(p1, p2, varargin)
%DISTANCEPOINTS Compute distance between two points.
%
% D = distancePoints(P1, P2)
% Return the Euclidean distance between points P1 and P2.
%
% If P1 and P2 are two arrays of points, result is a N1-by-N2 array
% containing distance between each point of P1 and each point of P2.
%
% D = distancePoints(P1, P2, NORM)
% Compute distance using the specified norm. NORM=2 corresponds to usual
% euclidean distance, NORM=1 corresponds to Manhattan distance, NORM=inf
% is assumed to correspond to maximum difference in coordinate. Other
% values (>0) can be specified.
%
% D = distancePoints(..., 'diag')
% compute only distances between P1(i,:) and P2(i,:).
%
% See also
% points2d, minDistancePoints, nndist, hausdorffDistance
%
% ------
% Author: David Legland
% E-mail: david.legland@inrae.fr
% Created: 2004-02-24
% Copyright 2004-2023 INRA - Cepia Software Platform
%% Setup options
% default values
diag = false;
norm = 2;
% check first argument: norm or diag
if ~isempty(varargin)
var = varargin{1};
if isnumeric(var)
norm = var;
elseif strncmp('diag', var, 4)
diag = true;
end
varargin(1) = [];
end
% check last argument: diag
if ~isempty(varargin)
var = varargin{1};
if strncmp('diag', var, 4)
diag = true;
end
end
% number of points in each array and their dimension
n1 = size(p1, 1);
n2 = size(p2, 1);
d = size(p1, 2);
if diag
% compute distance only for apparied couples of pixels
dist = zeros(n1, 1);
if norm == 2
% Compute euclidian distance. this is the default case
% Compute difference of coordinate for each pair of point
% and for each dimension. -> dist is a [n1*n2] array.
for i = 1:d
dist = dist + (p2(:,i)-p1(:,i)).^2;
end
dist = sqrt(dist);
elseif norm == inf
% infinite norm corresponds to maximal difference of coordinate
for i = 1:d
dist = max(dist, abs(p2(:,i)-p1(:,i)));
end
else
% compute distance using the specified norm.
for i = 1:d
dist = dist + power((abs(p2(:,i)-p1(:,i))), norm);
end
dist = power(dist, 1/norm);
end
else
% compute distance for all couples of pixels
dist = zeros(n1, n2);
if norm == 2
% Compute euclidian distance. This is the default case.
% Compute difference of coordinate for each pair of point
% and for each dimension. -> dist is a [n1*n2] array.
for i = 1:d
% equivalent to:
% dist = dist + ...
% (repmat(p1(:,i), [1 n2])-repmat(p2(:,i)', [n1 1])).^2;
dist = dist + bsxfun (@minus, p1(:,i), p2(:, i)').^2;
end
dist = sqrt(dist);
elseif norm == inf
% infinite norm corresponds to maximal difference of coordinate
for i = 1:d
dist = max(dist, abs(bsxfun (@minus, p1(:,i), p2(:, i)')));
end
else
% compute distance using the specified norm.
for i = 1:d
% equivalent to:
% dist = dist + power((abs(repmat(p1(:,i), [1 n2]) - ...
% repmat(p2(:,i)', [n1 1]))), norm);
dist = dist + power(abs(bsxfun(@minus, p1(:,i), p2(:, i)')), norm);
end
dist = power(dist, 1/norm);
end
end
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