Python utils.display_voxel() Examples

The following are 16 code examples of utils.display_voxel(). 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 utils , or try the search function .
Example #1
Source File: model_voxel_generation.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels):
  """Build the visualization grid with py_func."""
  quantity, img_height, img_width = input_images.shape[:3]
  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = input_images[index, :, :, :]
      gt_proj_ = gt_projs[index, :, :, :]
      pred_proj_ = pred_projs[index, :, :, :]
      pred_voxel_ = utils.display_voxel(pred_voxels[index, :, :, :, 0])
      pred_voxel_ = utils.resize_image(pred_voxel_, img_height, img_width)
      if col == 0:
        tmp_ = np.concatenate([input_img_, gt_proj_, pred_proj_, pred_voxel_],
                              1)
      else:
        tmp_ = np.concatenate(
            [tmp_, input_img_, gt_proj_, pred_proj_, pred_voxel_], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  out_grid = out_grid.astype(np.uint8)
  return out_grid
Example #2
Source File: model_voxel_generation.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels):
  """Build the visualization grid with py_func."""
  quantity, img_height, img_width = input_images.shape[:3]
  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = input_images[index, :, :, :]
      gt_proj_ = gt_projs[index, :, :, :]
      pred_proj_ = pred_projs[index, :, :, :]
      pred_voxel_ = utils.display_voxel(pred_voxels[index, :, :, :, 0])
      pred_voxel_ = utils.resize_image(pred_voxel_, img_height, img_width)
      if col == 0:
        tmp_ = np.concatenate([input_img_, gt_proj_, pred_proj_, pred_voxel_],
                              1)
      else:
        tmp_ = np.concatenate(
            [tmp_, input_img_, gt_proj_, pred_proj_, pred_voxel_], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  out_grid = out_grid.astype(np.uint8)
  return out_grid
Example #3
Source File: model_voxel_generation.py    From hands-detection with MIT License 6 votes vote down vote up 
def _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels):
  """Build the visualization grid with py_func."""
  quantity, img_height, img_width = input_images.shape[:3]
  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = input_images[index, :, :, :]
      gt_proj_ = gt_projs[index, :, :, :]
      pred_proj_ = pred_projs[index, :, :, :]
      pred_voxel_ = utils.display_voxel(pred_voxels[index, :, :, :, 0])
      pred_voxel_ = utils.resize_image(pred_voxel_, img_height, img_width)
      if col == 0:
        tmp_ = np.concatenate([input_img_, gt_proj_, pred_proj_, pred_voxel_],
                              1)
      else:
        tmp_ = np.concatenate(
            [tmp_, input_img_, gt_proj_, pred_proj_, pred_voxel_], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  out_grid = out_grid.astype(np.uint8)
  return out_grid
Example #4
Source File: model_voxel_generation.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels):
  """Build the visualization grid with py_func."""
  quantity, img_height, img_width = input_images.shape[:3]
  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = input_images[index, :, :, :]
      gt_proj_ = gt_projs[index, :, :, :]
      pred_proj_ = pred_projs[index, :, :, :]
      pred_voxel_ = utils.display_voxel(pred_voxels[index, :, :, :, 0])
      pred_voxel_ = utils.resize_image(pred_voxel_, img_height, img_width)
      if col == 0:
        tmp_ = np.concatenate([input_img_, gt_proj_, pred_proj_, pred_voxel_],
                              1)
      else:
        tmp_ = np.concatenate(
            [tmp_, input_img_, gt_proj_, pred_proj_, pred_voxel_], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  out_grid = out_grid.astype(np.uint8)
  return out_grid
Example #5
Source File: model_voxel_generation.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels):
  """Build the visualization grid with py_func."""
  quantity, img_height, img_width = input_images.shape[:3]
  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = input_images[index, :, :, :]
      gt_proj_ = gt_projs[index, :, :, :]
      pred_proj_ = pred_projs[index, :, :, :]
      pred_voxel_ = utils.display_voxel(pred_voxels[index, :, :, :, 0])
      pred_voxel_ = utils.resize_image(pred_voxel_, img_height, img_width)
      if col == 0:
        tmp_ = np.concatenate([input_img_, gt_proj_, pred_proj_, pred_voxel_],
                              1)
      else:
        tmp_ = np.concatenate(
            [tmp_, input_img_, gt_proj_, pred_proj_, pred_voxel_], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  out_grid = out_grid.astype(np.uint8)
  return out_grid
Example #6
Source File: model_voxel_generation.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels):
  """Build the visualization grid with py_func."""
  quantity, img_height, img_width = input_images.shape[:3]
  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = input_images[index, :, :, :]
      gt_proj_ = gt_projs[index, :, :, :]
      pred_proj_ = pred_projs[index, :, :, :]
      pred_voxel_ = utils.display_voxel(pred_voxels[index, :, :, :, 0])
      pred_voxel_ = utils.resize_image(pred_voxel_, img_height, img_width)
      if col == 0:
        tmp_ = np.concatenate([input_img_, gt_proj_, pred_proj_, pred_voxel_],
                              1)
      else:
        tmp_ = np.concatenate(
            [tmp_, input_img_, gt_proj_, pred_proj_, pred_voxel_], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  out_grid = out_grid.astype(np.uint8)
  return out_grid
Example #7
Source File: model_voxel_generation.py    From models with Apache License 2.0 6 votes vote down vote up 
def _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels):
  """Build the visualization grid with py_func."""
  quantity, img_height, img_width = input_images.shape[:3]
  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = input_images[index, :, :, :]
      gt_proj_ = gt_projs[index, :, :, :]
      pred_proj_ = pred_projs[index, :, :, :]
      pred_voxel_ = utils.display_voxel(pred_voxels[index, :, :, :, 0])
      pred_voxel_ = utils.resize_image(pred_voxel_, img_height, img_width)
      if col == 0:
        tmp_ = np.concatenate([input_img_, gt_proj_, pred_proj_, pred_voxel_],
                              1)
      else:
        tmp_ = np.concatenate(
            [tmp_, input_img_, gt_proj_, pred_proj_, pred_voxel_], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  out_grid = out_grid.astype(np.uint8)
  return out_grid
Example #8
Source File: model_voxel_generation.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def _build_image_grid(input_images, gt_projs, pred_projs, pred_voxels):
  """Build the visualization grid with py_func."""
  quantity, img_height, img_width = input_images.shape[:3]
  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = input_images[index, :, :, :]
      gt_proj_ = gt_projs[index, :, :, :]
      pred_proj_ = pred_projs[index, :, :, :]
      pred_voxel_ = utils.display_voxel(pred_voxels[index, :, :, :, 0])
      pred_voxel_ = utils.resize_image(pred_voxel_, img_height, img_width)
      if col == 0:
        tmp_ = np.concatenate([input_img_, gt_proj_, pred_proj_, pred_voxel_],
                              1)
      else:
        tmp_ = np.concatenate(
            [tmp_, input_img_, gt_proj_, pred_proj_, pred_voxel_], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  out_grid = out_grid.astype(np.uint8)
  return out_grid
Example #9
Source File: model_ptn.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def _build_image_grid(input_images,
                      gt_projs,
                      pred_projs,
                      input_voxels,
                      output_voxels,
                      vis_size=128):
  """Builds a grid image by concatenating the input images."""
  quantity = input_images.shape[0]

  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = utils.resize_image(input_images[index, :, :, :], vis_size,
                                      vis_size)
      gt_proj_ = utils.resize_image(gt_projs[index, :, :, :], vis_size,
                                    vis_size)
      pred_proj_ = utils.resize_image(pred_projs[index, :, :, :], vis_size,
                                      vis_size)
      gt_voxel_vis = utils.resize_image(
          utils.display_voxel(input_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      pred_voxel_vis = utils.resize_image(
          utils.display_voxel(output_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      if col == 0:
        tmp_ = np.concatenate(
            [input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis], 1)
      else:
        tmp_ = np.concatenate([
            tmp_, input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis
        ], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  return out_grid
Example #10
Source File: model_ptn.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def _build_image_grid(input_images,
                      gt_projs,
                      pred_projs,
                      input_voxels,
                      output_voxels,
                      vis_size=128):
  """Builds a grid image by concatenating the input images."""
  quantity = input_images.shape[0]

  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = utils.resize_image(input_images[index, :, :, :], vis_size,
                                      vis_size)
      gt_proj_ = utils.resize_image(gt_projs[index, :, :, :], vis_size,
                                    vis_size)
      pred_proj_ = utils.resize_image(pred_projs[index, :, :, :], vis_size,
                                      vis_size)
      gt_voxel_vis = utils.resize_image(
          utils.display_voxel(input_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      pred_voxel_vis = utils.resize_image(
          utils.display_voxel(output_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      if col == 0:
        tmp_ = np.concatenate(
            [input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis], 1)
      else:
        tmp_ = np.concatenate([
            tmp_, input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis
        ], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  return out_grid
Example #11
Source File: model_ptn.py    From hands-detection with MIT License 5 votes vote down vote up 
def _build_image_grid(input_images,
                      gt_projs,
                      pred_projs,
                      input_voxels,
                      output_voxels,
                      vis_size=128):
  """Builds a grid image by concatenating the input images."""
  quantity = input_images.shape[0]

  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = utils.resize_image(input_images[index, :, :, :], vis_size,
                                      vis_size)
      gt_proj_ = utils.resize_image(gt_projs[index, :, :, :], vis_size,
                                    vis_size)
      pred_proj_ = utils.resize_image(pred_projs[index, :, :, :], vis_size,
                                      vis_size)
      gt_voxel_vis = utils.resize_image(
          utils.display_voxel(input_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      pred_voxel_vis = utils.resize_image(
          utils.display_voxel(output_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      if col == 0:
        tmp_ = np.concatenate(
            [input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis], 1)
      else:
        tmp_ = np.concatenate([
            tmp_, input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis
        ], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  return out_grid
Example #12
Source File: model_ptn.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def _build_image_grid(input_images,
                      gt_projs,
                      pred_projs,
                      input_voxels,
                      output_voxels,
                      vis_size=128):
  """Builds a grid image by concatenating the input images."""
  quantity = input_images.shape[0]

  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = utils.resize_image(input_images[index, :, :, :], vis_size,
                                      vis_size)
      gt_proj_ = utils.resize_image(gt_projs[index, :, :, :], vis_size,
                                    vis_size)
      pred_proj_ = utils.resize_image(pred_projs[index, :, :, :], vis_size,
                                      vis_size)
      gt_voxel_vis = utils.resize_image(
          utils.display_voxel(input_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      pred_voxel_vis = utils.resize_image(
          utils.display_voxel(output_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      if col == 0:
        tmp_ = np.concatenate(
            [input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis], 1)
      else:
        tmp_ = np.concatenate([
            tmp_, input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis
        ], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  return out_grid
Example #13
Source File: model_ptn.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def _build_image_grid(input_images,
                      gt_projs,
                      pred_projs,
                      input_voxels,
                      output_voxels,
                      vis_size=128):
  """Builds a grid image by concatenating the input images."""
  quantity = input_images.shape[0]

  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = utils.resize_image(input_images[index, :, :, :], vis_size,
                                      vis_size)
      gt_proj_ = utils.resize_image(gt_projs[index, :, :, :], vis_size,
                                    vis_size)
      pred_proj_ = utils.resize_image(pred_projs[index, :, :, :], vis_size,
                                      vis_size)
      gt_voxel_vis = utils.resize_image(
          utils.display_voxel(input_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      pred_voxel_vis = utils.resize_image(
          utils.display_voxel(output_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      if col == 0:
        tmp_ = np.concatenate(
            [input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis], 1)
      else:
        tmp_ = np.concatenate([
            tmp_, input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis
        ], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  return out_grid
Example #14
Source File: model_ptn.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def _build_image_grid(input_images,
                      gt_projs,
                      pred_projs,
                      input_voxels,
                      output_voxels,
                      vis_size=128):
  """Builds a grid image by concatenating the input images."""
  quantity = input_images.shape[0]

  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = utils.resize_image(input_images[index, :, :, :], vis_size,
                                      vis_size)
      gt_proj_ = utils.resize_image(gt_projs[index, :, :, :], vis_size,
                                    vis_size)
      pred_proj_ = utils.resize_image(pred_projs[index, :, :, :], vis_size,
                                      vis_size)
      gt_voxel_vis = utils.resize_image(
          utils.display_voxel(input_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      pred_voxel_vis = utils.resize_image(
          utils.display_voxel(output_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      if col == 0:
        tmp_ = np.concatenate(
            [input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis], 1)
      else:
        tmp_ = np.concatenate([
            tmp_, input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis
        ], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  return out_grid
Example #15
Source File: model_ptn.py    From models with Apache License 2.0 5 votes vote down vote up 
def _build_image_grid(input_images,
                      gt_projs,
                      pred_projs,
                      input_voxels,
                      output_voxels,
                      vis_size=128):
  """Builds a grid image by concatenating the input images."""
  quantity = input_images.shape[0]

  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = utils.resize_image(input_images[index, :, :, :], vis_size,
                                      vis_size)
      gt_proj_ = utils.resize_image(gt_projs[index, :, :, :], vis_size,
                                    vis_size)
      pred_proj_ = utils.resize_image(pred_projs[index, :, :, :], vis_size,
                                      vis_size)
      gt_voxel_vis = utils.resize_image(
          utils.display_voxel(input_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      pred_voxel_vis = utils.resize_image(
          utils.display_voxel(output_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      if col == 0:
        tmp_ = np.concatenate(
            [input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis], 1)
      else:
        tmp_ = np.concatenate([
            tmp_, input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis
        ], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  return out_grid
Example #16
Source File: model_ptn.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def _build_image_grid(input_images,
                      gt_projs,
                      pred_projs,
                      input_voxels,
                      output_voxels,
                      vis_size=128):
  """Builds a grid image by concatenating the input images."""
  quantity = input_images.shape[0]

  for row in xrange(int(quantity / 3)):
    for col in xrange(3):
      index = row * 3 + col
      input_img_ = utils.resize_image(input_images[index, :, :, :], vis_size,
                                      vis_size)
      gt_proj_ = utils.resize_image(gt_projs[index, :, :, :], vis_size,
                                    vis_size)
      pred_proj_ = utils.resize_image(pred_projs[index, :, :, :], vis_size,
                                      vis_size)
      gt_voxel_vis = utils.resize_image(
          utils.display_voxel(input_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      pred_voxel_vis = utils.resize_image(
          utils.display_voxel(output_voxels[index, :, :, :, 0]), vis_size,
          vis_size)
      if col == 0:
        tmp_ = np.concatenate(
            [input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis], 1)
      else:
        tmp_ = np.concatenate([
            tmp_, input_img_, gt_proj_, pred_proj_, gt_voxel_vis, pred_voxel_vis
        ], 1)
    if row == 0:
      out_grid = tmp_
    else:
      out_grid = np.concatenate([out_grid, tmp_], 0)

  return out_grid