sp.ndimage.zoom / tf.image.resize_images alternative

It would be nice to have a batched image resize (interpolation) operation implemented in JAX, afaik these are scipy.ndimage.zoom in scipy or tensorflow.image.resize_images in Tensorflow.

[hawkinsp]

I spent a bit of time looking at this. My guess is that perhaps skimage.transform.resize (or rescale) is a good choice? It looks to me like scipy.ndimage.zoom suffers from some of the same pixel-centering bugs that TF’s resize bilinear used to suffer from (https://hackernoon.com/how-tensorflows-tf-image-resize-stole-60-days-of-my-life-aba5eb093f35)

[mattjj]

Good idea! Is scipy.ndimage.zoom the API you want?

In the meantime here’s a pure-NumPy snippet you can use:

def interpolate_bilinear(im, rows, cols):
  # based on http://stackoverflow.com/a/12729229
  col_lo = np.floor(cols).astype(int)
  col_hi = col_lo + 1
  row_lo = np.floor(rows).astype(int)
  row_hi = row_lo + 1

  nrows, ncols = im.shape[-3:-1]
  def cclip(cols): return np.clip(cols, 0, ncols - 1)
  def rclip(rows): return np.clip(rows, 0, nrows - 1)
  Ia = im[..., rclip(row_lo), cclip(col_lo), :]
  Ib = im[..., rclip(row_hi), cclip(col_lo), :]
  Ic = im[..., rclip(row_lo), cclip(col_hi), :]
  Id = im[..., rclip(row_hi), cclip(col_hi), :]

  wa = np.expand_dims((col_hi - cols) * (row_hi - rows), -1)
  wb = np.expand_dims((col_hi - cols) * (rows - row_lo), -1)
  wc = np.expand_dims((cols - col_lo) * (row_hi - rows), -1)
  wd = np.expand_dims((cols - col_lo) * (rows - row_lo), -1)

  return wa*Ia + wb*Ib + wc*Ic + wd*Id