Exemplos de ZSwcResampler em C++ (Cpp)

Exemplo n.º 1

ZSwcTree* ZSwcGenerator::createSwc(
    const ZVoxelArray &voxelArray, ZSwcGenerator::EPostProcess option)
{
  if (option == REGION_SAMPLING) {
    return createSwcByRegionSampling(voxelArray);
  }

  size_t startIndex = 0;
  size_t endIndex = voxelArray.size() - 1;

  Swc_Tree *tree = New_Swc_Tree();

  const std::vector<ZVoxel> &voxelData = voxelArray.getInternalData();
  ZVoxel prevVoxel = voxelData[startIndex];

  Swc_Tree_Node *tn = New_Swc_Tree_Node();
  SwcTreeNode::setPos(tn, prevVoxel.x(), prevVoxel.y(), prevVoxel.z());
  SwcTreeNode::setRadius(tn, prevVoxel.value());

  Swc_Tree_Node *prevTn = tn;

  for (size_t i = startIndex + 1; i < endIndex; i++) {
    double dist = voxelData[i].distanceTo(prevVoxel);
    bool sampling = true;

    if (option == SPARSE_SAMPLING) {
      if (dist < prevVoxel.value()) {
        sampling = false;
      }
    }

    if (sampling) {
      tn = New_Swc_Tree_Node();

      SwcTreeNode::setPos(tn, voxelData[i].x(), voxelData[i].y(), voxelData[i].z());
      SwcTreeNode::setRadius(tn, voxelData[i].value());
      Swc_Tree_Node_Set_Parent(prevTn, tn);
      prevTn = tn;
      prevVoxel = voxelData[i];
    }
  }

  if (endIndex - startIndex > 0) { //last node
    tn = New_Swc_Tree_Node();

    SwcTreeNode::setPos(tn, voxelData[endIndex].x(), voxelData[endIndex].y(),
                        voxelData[endIndex].z());
    SwcTreeNode::setRadius(tn, voxelData[endIndex].value());
    Swc_Tree_Node_Set_Parent(prevTn, tn);
    /*
    if (SwcTreeNode::hasOverlap(prevTn, tn) && SwcTreeNode::hasChild(prevTn)) {
      SwcTreeNode::mergeToParent(prevTn);
    }
    */
  }

  tree->root = tn;

  ZSwcTree *treeWrapper = new ZSwcTree;
  treeWrapper->setData(tree);

  if (option == OPTIMAL_SAMPLING) {
    ZSwcResampler resampler;
    resampler.optimalDownsample(treeWrapper);
  }

  return treeWrapper;
}

Exemplo n.º 2

ZSwcTree* ZNeuronTracer::trace(Stack *stack, bool doResampleAfterTracing)
{
  startProgress();

  ZSwcTree *tree = NULL;

  //Extract seeds
  //First mask
  std::cout << "Binarizing ..." << std::endl;

  /* <bw> allocated */
  Stack *bw = binarize(stack);
  C_Stack::translate(bw, GREY, 1);

  advanceProgress(0.05);

  std::cout << "Removing noise ..." << std::endl;

  /* <mask> allocated */
  Stack *mask = bwsolid(bw);
  advanceProgress(0.05);

  /* <bw> freed */
  C_Stack::kill(bw);

  //Thin line mask
  /* <mask2> allocated */
  Stack *mask2 = NULL;

  if (m_enhancingMask) {
    std::cout << "Enhancing thin branches ..." << std::endl;
    mask2 = enhanceLine(stack);
    advanceProgress(0.05);
  }

  if (mask2 != NULL) {
    std::cout << "Making mask for thin branches ..." << std::endl;
    ZStackBinarizer binarizer;
    binarizer.setMethod(ZStackBinarizer::BM_LOCMAX);
    binarizer.setRetryCount(5);
    binarizer.setMinObjectSize(27);

    if (binarizer.binarize(mask2) == false) {
      std::cout << "Thresholding failed" << std::endl;
      C_Stack::kill(mask2);
      mask2 = NULL;
    }
  }

  /* <mask2> freed */
  if (mask2 != NULL) {
    C_Stack::translate(mask2, GREY, 1);
    Stack_Or(mask, mask2, mask);
    C_Stack::kill(mask2);
    mask2 = NULL;
  }
  advanceProgress(0.05);

  //Trace each seed
  std::cout << "Extracting seed points ..." << std::endl;

  /* <seedPointArray> allocated */
  Geo3d_Scalar_Field *seedPointArray = extractSeed(mask);
  m_mask = mask;

  advanceProgress(0.05);

  std::cout << "Sorting seeds ..." << std::endl;
  ZNeuronTraceSeeder seeder;
  setTraceScoreThreshold(TRACING_SEED);
  m_baseMask = seeder.sortSeed(seedPointArray, stack, m_traceWorkspace);

#ifdef _DEBUG_2
  C_Stack::write(GET_TEST_DATA_DIR + "/test.tif", m_baseMask);
#endif

  advanceProgress(0.1);

  /* <seedPointArray> freed */
  Kill_Geo3d_Scalar_Field(seedPointArray);

  std::vector<Local_Neuroseg>& locsegArray = seeder.getSeedArray();
  std::vector<double>& scoreArray = seeder.getScoreArray();

  std::cout << "Tracing ..." << std::endl;

  /* <chainArray> allocated */

  std::vector<Locseg_Chain*> chainArray = trace(stack, locsegArray, scoreArray);

  if (m_recover > 0) {
    std::vector<Locseg_Chain*> newChainArray = recover(stack);
    chainArray.insert(
          chainArray.end(), newChainArray.begin(), newChainArray.end());
  }
  advanceProgress(0.1);

  chainArray = screenChain(stack, chainArray);
  advanceProgress(0.3);

  /* <mask2> freed */
//  C_Stack::kill(mask);

  std::cout << "Reconstructing ..." << std::endl;
  ZNeuronConstructor constructor;
  constructor.setWorkspace(m_connWorkspace);
  constructor.setSignal(stack);

  //Create neuron structure

  BOOL oldSpTest = m_connWorkspace->sp_test;
  if (chainArray.size() > 1000) {
    std::cout << "Too many chains: " << chainArray.size() << std::endl;
    std::cout << "Turn off shortest path test" << std::endl;
    m_connWorkspace->sp_test = FALSE;
  }

  /* free <chainArray> */
  tree = constructor.reconstruct(chainArray);

  m_connWorkspace->sp_test = oldSpTest;

  advanceProgress(0.1);

  //Post process
  Swc_Tree_Remove_Zigzag(tree->data());
  Swc_Tree_Tune_Branch(tree->data());
  Swc_Tree_Remove_Spur(tree->data());
  Swc_Tree_Merge_Close_Node(tree->data(), 0.01);
  Swc_Tree_Remove_Overshoot(tree->data());

  if (doResampleAfterTracing) {
    ZSwcResampler resampler;
    resampler.optimalDownsample(tree);
  }
  advanceProgress(0.1);

  std::cout << "Done!" << std::endl;
  endProgress();

  return tree;
}