Python skimage.measure.marching_cubes_classic() Examples

The following are 8 code examples of skimage.measure.marching_cubes_classic(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module skimage.measure , or try the search function .
Example #1
Source File: utils.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def visualize_voxel_spectral(points, vis_size=128):
  """Function to visualize voxel (spectral)."""
  points = np.rint(points)
  points = np.swapaxes(points, 0, 2)
  fig = p.figure(figsize=(1, 1), dpi=vis_size)
  verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1))
  ax = fig.add_subplot(111, projection='3d')
  ax.plot_trisurf(
      verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1)
  ax.set_axis_off()
  fig.tight_layout(pad=0)
  fig.canvas.draw()
  data = np.fromstring(
      fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
          vis_size, vis_size, 3)
  p.close('all')
  return data
Example #2
Source File: utils.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def visualize_voxel_spectral(points, vis_size=128):
  """Function to visualize voxel (spectral)."""
  points = np.rint(points)
  points = np.swapaxes(points, 0, 2)
  fig = p.figure(figsize=(1, 1), dpi=vis_size)
  verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1))
  ax = fig.add_subplot(111, projection='3d')
  ax.plot_trisurf(
      verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1)
  ax.set_axis_off()
  fig.tight_layout(pad=0)
  fig.canvas.draw()
  data = np.fromstring(
      fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
          vis_size, vis_size, 3)
  p.close('all')
  return data
Example #3
Source File: utils.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def visualize_voxel_spectral(points, vis_size=128):
  """Function to visualize voxel (spectral)."""
  points = np.rint(points)
  points = np.swapaxes(points, 0, 2)
  fig = p.figure(figsize=(1, 1), dpi=vis_size)
  verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1))
  ax = fig.add_subplot(111, projection='3d')
  ax.plot_trisurf(
      verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1)
  ax.set_axis_off()
  fig.tight_layout(pad=0)
  fig.canvas.draw()
  data = np.fromstring(
      fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
          vis_size, vis_size, 3)
  p.close('all')
  return data
Example #4
Source File: utils.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def visualize_voxel_spectral(points, vis_size=128):
  """Function to visualize voxel (spectral)."""
  points = np.rint(points)
  points = np.swapaxes(points, 0, 2)
  fig = p.figure(figsize=(1, 1), dpi=vis_size)
  verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1))
  ax = fig.add_subplot(111, projection='3d')
  ax.plot_trisurf(
      verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1)
  ax.set_axis_off()
  fig.tight_layout(pad=0)
  fig.canvas.draw()
  data = np.fromstring(
      fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
          vis_size, vis_size, 3)
  p.close('all')
  return data
Example #5
Source File: data_processings.py    From PyTorch-Luna16 with Apache License 2.0 6 votes vote down vote up 
def plot_3d(image, threshold=-300):
    # Position the scan upright,
    # so the head of the patient would be at the top facing the camera
    p = image.transpose(2, 1, 0)
    p = p[:, :, ::-1]

    verts, faces = measure.marching_cubes_classic(p, threshold)

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(111, projection='3d')

    # Fancy indexing: `verts[faces]` to generate a collection of triangles
    mesh = Poly3DCollection(verts[faces], alpha=0.70)
    face_color = [0.45, 0.45, 0.8]
    mesh.set_facecolor(face_color)
    ax.add_collection3d(mesh)

    ax.set_xlim(0, p.shape[0])
    ax.set_ylim(0, p.shape[1])
    ax.set_zlim(0, p.shape[2])

    plt.show()


# plot_3d(pix_resampled, 400)
Example #6
Source File: utils.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def visualize_voxel_spectral(points, vis_size=128):
  """Function to visualize voxel (spectral)."""
  points = np.rint(points)
  points = np.swapaxes(points, 0, 2)
  fig = p.figure(figsize=(1, 1), dpi=vis_size)
  verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1))
  ax = fig.add_subplot(111, projection='3d')
  ax.plot_trisurf(
      verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1)
  ax.set_axis_off()
  fig.tight_layout(pad=0)
  fig.canvas.draw()
  data = np.fromstring(
      fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
          vis_size, vis_size, 3)
  p.close('all')
  return data
Example #7
Source File: utils.py    From models with Apache License 2.0 6 votes vote down vote up 
def visualize_voxel_spectral(points, vis_size=128):
  """Function to visualize voxel (spectral)."""
  points = np.rint(points)
  points = np.swapaxes(points, 0, 2)
  fig = p.figure(figsize=(1, 1), dpi=vis_size)
  verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1))
  ax = fig.add_subplot(111, projection='3d')
  ax.plot_trisurf(
      verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1)
  ax.set_axis_off()
  fig.tight_layout(pad=0)
  fig.canvas.draw()
  data = np.fromstring(
      fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
          vis_size, vis_size, 3)
  p.close('all')
  return data
Example #8
Source File: utils.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def visualize_voxel_spectral(points, vis_size=128):
  """Function to visualize voxel (spectral)."""
  points = np.rint(points)
  points = np.swapaxes(points, 0, 2)
  fig = p.figure(figsize=(1, 1), dpi=vis_size)
  verts, faces = measure.marching_cubes_classic(points, 0, spacing=(0.1, 0.1, 0.1))
  ax = fig.add_subplot(111, projection='3d')
  ax.plot_trisurf(
      verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1)
  ax.set_axis_off()
  fig.tight_layout(pad=0)
  fig.canvas.draw()
  data = np.fromstring(
      fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
          vis_size, vis_size, 3)
  p.close('all')
  return data