GCC Code Coverage Report
Directory: ./ Exec Total Coverage
File: src/ten/tendExpand.c Lines: 18 47 38.3 %
Date: 2017-05-26 Branches: 1 40 2.5 %

Line Branch Exec Source
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/*
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  Teem: Tools to process and visualize scientific data and images             .
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  Copyright (C) 2013, 2012, 2011, 2010, 2009  University of Chicago
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  Copyright (C) 2008, 2007, 2006, 2005  Gordon Kindlmann
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  Copyright (C) 2004, 2003, 2002, 2001, 2000, 1999, 1998  University of Utah
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  This library is free software; you can redistribute it and/or
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  modify it under the terms of the GNU Lesser General Public License
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  (LGPL) as published by the Free Software Foundation; either
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  version 2.1 of the License, or (at your option) any later version.
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  The terms of redistributing and/or modifying this software also
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  include exceptions to the LGPL that facilitate static linking.
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  This library is distributed in the hope that it will be useful,
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  but WITHOUT ANY WARRANTY; without even the implied warranty of
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  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
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  Lesser General Public License for more details.
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  You should have received a copy of the GNU Lesser General Public License
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  along with this library; if not, write to Free Software Foundation, Inc.,
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  51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
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*/
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#include "ten.h"
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#include "privateTen.h"
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#define INFO "Converts masked non-redundant tensor images to redundant"
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static const char *_tend_expandInfoL =
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  (INFO
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   ". For images of 3D tensors, this converts from a 7-value tensor "
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   "starting with the confidence/mask value "
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   "(conf, Dxx, Dxy, Dxz, Dyy, Dyz, Dzz) to "
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   "a 9-value tensor with the full matrix "
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   "(Dxx, Dxy, Dxz, Dxy, Dyy, Dyz, Dxz, Dyz, Dzz). "
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   "This is set to all zeros when the confidence is below the given "
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   "threshold. For images of 2D tensors, the conversion is from "
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   "(conf, Dxx, Dxy, Dyy) to (Dxx, Dxy, Dxy, Dyy). " );
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int
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tend_expandMain(int argc, const char **argv, const char *me,
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                hestParm *hparm) {
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  int pret;
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  hestOpt *hopt = NULL;
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1
  char *perr, *err;
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  airArray *mop;
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1
  Nrrd *nin, *nout;
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1
  char *outS;
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  int orientRed, orientRedWithOrigin, mfRed;
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1
  float scale, thresh;
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  hestOptAdd(&hopt, "t", "thresh", airTypeFloat, 1, 1, &thresh, "0.5",
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             "confidence level to threshold output tensors at.  Should "
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             "be between 0.0 and 1.0.");
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  hestOptAdd(&hopt, "s", "scale", airTypeFloat, 1, 1, &scale, "1.0",
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             "how to scale values before saving as 9-value tensor.  Useful "
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             "for visualization tools which assume certain characteristic "
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             "ranges of eigenvalues");
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  hestOptAdd(&hopt, "unmf", NULL, airTypeInt, 0, 0, &mfRed, NULL,
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             "apply and remove the measurement frame, if it exists");
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  hestOptAdd(&hopt, "ro", NULL, airTypeInt, 0, 0, &orientRed, NULL,
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             "reduce general image orientation to axis-aligned spacings");
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  hestOptAdd(&hopt, "roo", NULL, airTypeInt, 0, 0,
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             &orientRedWithOrigin, NULL,
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             "reduce general image orientation to axis-aligned spacings, "
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             "while also making some effort to set axis mins from "
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             "space origin");
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  hestOptAdd(&hopt, "i", "nin", airTypeOther, 1, 1, &nin, "-",
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             "input diffusion tensor volume, with 7 values per sample",
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             NULL, NULL, nrrdHestNrrd);
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  hestOptAdd(&hopt, "o", "nout", airTypeString, 1, 1, &outS, NULL,
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             "output tensor volume, with the 9 matrix components per sample");
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  mop = airMopNew();
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  airMopAdd(mop, hopt, (airMopper)hestOptFree, airMopAlways);
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  USAGE(_tend_expandInfoL);
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  PARSE();
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  airMopAdd(mop, hopt, (airMopper)hestParseFree, airMopAlways);
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  nout = nrrdNew();
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  airMopAdd(mop, nout, (airMopper)nrrdNuke, airMopAlways);
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  if (mfRed
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      && 3 == nin->spaceDim
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      && AIR_EXISTS(nin->measurementFrame[0][0])) {
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    if (tenMeasurementFrameReduce(nin, nin)) {
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      airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways);
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      fprintf(stderr, "%s: trouble with measurement frame:\n%s\n", me, err);
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      airMopError(mop); return 1;
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    }
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  }
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  if (4 == nin->axis[0].size
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      ? tenExpand2D(nout, nin, scale, thresh)
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      : tenExpand(nout, nin, scale, thresh)) {
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    airMopAdd(mop, err=biffGetDone(TEN), airFree, airMopAlways);
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    fprintf(stderr, "%s: trouble expanding tensors:\n%s\n", me, err);
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    airMopError(mop); return 1;
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  }
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  if (orientRedWithOrigin || orientRed) {
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    if (nrrdOrientationReduce(nout, nout,
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                              orientRedWithOrigin
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                              ? AIR_TRUE
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                              : AIR_FALSE)) {
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      airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways);
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      fprintf(stderr, "%s: trouble unorienting:\n%s\n", me, err);
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      airMopError(mop); return 1;
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    }
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  }
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  if (nrrdSave(outS, nout, NULL)) {
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    airMopAdd(mop, err=biffGetDone(NRRD), airFree, airMopAlways);
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    fprintf(stderr, "%s: trouble writing:\n%s\n", me, err);
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    airMopError(mop); return 1;
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  }
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  airMopOkay(mop);
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  return 0;
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1
}
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TEND_CMD(expand, INFO);