tensorflow: how to rotate an image for data augmentation?

Question

In tensorflow, I would like to rotate an image from a random angle, for data augmentation. But I don't find this transformation in the tf.image module.

Answer 1

tfa has been deprecated, one can use preprocessing layer [RandomRotation][1]:

tf.keras.layers.RandomRotation(factor)

factor=(-0.2, 0.3) results in an output rotation by a random amount in the range [-20% * 2pi, 30% * 2pi]. factor=0.2 results in an output rotating by a random amount in the range [-20% * 2pi, 20% * 2pi]

[OLD] for tensorflow 2.0:

import tensorflow_addons as tfa
tfa.image.transform_ops.rotate(image, radian)

Answer 2

tf.contrib is not available in tensorflow 2.

For tensorflow >= 2.* the following can be used:

tf.keras.preprocessing.image.random_rotation(x, rg, row_axis=1,col_axis=2, channel_axis=0,fill_mode='nearest', cval=0., interpolation_order=1);

you can find the documantation here: https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/random_rotation

Answer 3

For rotating an image or a batch of images counter-clockwise by multiples of 90 degrees, you can use tf.image.rot90(image,k=1,name=None).

k denotes the number of 90 degrees rotations you want to make.

In case of a single image, image is a 3-D Tensor of shape [height, width, channels] and in case of a batch of images, image is a 4-D Tensor of shape [batch, height, width, channels]

Answer 4

Rotation and cropping in TensorFlow

I personally needed image rotation and cropping out black borders functions in TensorFlow as below. And I could implement this function as below.

def _rotate_and_crop(image, output_height, output_width, rotation_degree, do_crop):
    """Rotate the given image with the given rotation degree and crop for the black edges if necessary
    Args:
        image: A `Tensor` representing an image of arbitrary size.
        output_height: The height of the image after preprocessing.
        output_width: The width of the image after preprocessing.
        rotation_degree: The degree of rotation on the image.
        do_crop: Do cropping if it is True.
    Returns:
        A rotated image.
    """

    # Rotate the given image with the given rotation degree
    if rotation_degree != 0:
        image = tf.contrib.image.rotate(image, math.radians(rotation_degree), interpolation='BILINEAR')

        # Center crop to ommit black noise on the edges
        if do_crop == True:
            lrr_width, lrr_height = _largest_rotated_rect(output_height, output_width, math.radians(rotation_degree))
            resized_image = tf.image.central_crop(image, float(lrr_height)/output_height)    
            image = tf.image.resize_images(resized_image, [output_height, output_width], method=tf.image.ResizeMethod.BILINEAR, align_corners=False)

    return image

def _largest_rotated_rect(w, h, angle):
    """
    Given a rectangle of size wxh that has been rotated by 'angle' (in
    radians), computes the width and height of the largest possible
    axis-aligned rectangle within the rotated rectangle.
    Original JS code by 'Andri' and Magnus Hoff from Stack Overflow
    Converted to Python by Aaron Snoswell
    Source: http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
    """

    quadrant = int(math.floor(angle / (math.pi / 2))) & 3
    sign_alpha = angle if ((quadrant & 1) == 0) else math.pi - angle
    alpha = (sign_alpha % math.pi + math.pi) % math.pi

    bb_w = w * math.cos(alpha) + h * math.sin(alpha)
    bb_h = w * math.sin(alpha) + h * math.cos(alpha)

    gamma = math.atan2(bb_w, bb_w) if (w < h) else math.atan2(bb_w, bb_w)

    delta = math.pi - alpha - gamma

    length = h if (w < h) else w

    d = length * math.cos(alpha)
    a = d * math.sin(alpha) / math.sin(delta)

    y = a * math.cos(gamma)
    x = y * math.tan(gamma)

    return (
        bb_w - 2 * x,
        bb_h - 2 * y
    )

If you need further implementation of example and visualization in TensorFlow, you can use this repository. I hope this could be helpful to other people.

Answer 5

Update: see @astromme's answer below. Tensorflow now supports rotating images natively.

What you can do while there is no native method in tensorflow is something like this:

from PIL import Image
sess = tf.InteractiveSession()

# Pass image tensor object to a PIL image
image = Image.fromarray(image.eval())

# Use PIL or other library of the sort to rotate
rotated = Image.Image.rotate(image, degrees)

# Convert rotated image back to tensor
rotated_tensor = tf.convert_to_tensor(np.array(rotated))

Answer 6

This can be done in tensorflow now:

tf.contrib.image.rotate(images, degrees * math.pi / 180, interpolation='BILINEAR')

Answer 7

Here's the @zimmermc answer updated to Tensorflow v0.12

Changes:

• pack() is now stack()

• order of split parameters reversed

  def rotate_image_tensor(image, angle, mode='white'):
      """
      Rotates a 3D tensor (HWD), which represents an image by given radian angle.
  
      New image has the same size as the input image.
  
      mode controls what happens to border pixels.
      mode = 'black' results in black bars (value 0 in unknown areas)
      mode = 'white' results in value 255 in unknown areas
      mode = 'ones' results in value 1 in unknown areas
      mode = 'repeat' keeps repeating the closest pixel known
      """
      s = image.get_shape().as_list()
      assert len(s) == 3, "Input needs to be 3D."
      assert (mode == 'repeat') or (mode == 'black') or (mode == 'white') or (mode == 'ones'), "Unknown boundary mode."
      image_center = [np.floor(x/2) for x in s]
  
      # Coordinates of new image
      coord1 = tf.range(s[0])
      coord2 = tf.range(s[1])
  
      # Create vectors of those coordinates in order to vectorize the image
      coord1_vec = tf.tile(coord1, [s[1]])
  
      coord2_vec_unordered = tf.tile(coord2, [s[0]])
      coord2_vec_unordered = tf.reshape(coord2_vec_unordered, [s[0], s[1]])
      coord2_vec = tf.reshape(tf.transpose(coord2_vec_unordered, [1, 0]), [-1])
  
      # center coordinates since rotation center is supposed to be in the image center
      coord1_vec_centered = coord1_vec - image_center[0]
      coord2_vec_centered = coord2_vec - image_center[1]
  
      coord_new_centered = tf.cast(tf.stack([coord1_vec_centered, coord2_vec_centered]), tf.float32)
  
      # Perform backward transformation of the image coordinates
      rot_mat_inv = tf.dynamic_stitch([[0], [1], [2], [3]], [tf.cos(angle), tf.sin(angle), -tf.sin(angle), tf.cos(angle)])
      rot_mat_inv = tf.reshape(rot_mat_inv, shape=[2, 2])
      coord_old_centered = tf.matmul(rot_mat_inv, coord_new_centered)
  
      # Find nearest neighbor in old image
      coord1_old_nn = tf.cast(tf.round(coord_old_centered[0, :] + image_center[0]), tf.int32)
      coord2_old_nn = tf.cast(tf.round(coord_old_centered[1, :] + image_center[1]), tf.int32)
  
      # Clip values to stay inside image coordinates
      if mode == 'repeat':
          coord_old1_clipped = tf.minimum(tf.maximum(coord1_old_nn, 0), s[0]-1)
          coord_old2_clipped = tf.minimum(tf.maximum(coord2_old_nn, 0), s[1]-1)
      else:
          outside_ind1 = tf.logical_or(tf.greater(coord1_old_nn, s[0]-1), tf.less(coord1_old_nn, 0))
          outside_ind2 = tf.logical_or(tf.greater(coord2_old_nn, s[1]-1), tf.less(coord2_old_nn, 0))
          outside_ind = tf.logical_or(outside_ind1, outside_ind2)
  
          coord_old1_clipped = tf.boolean_mask(coord1_old_nn, tf.logical_not(outside_ind))
          coord_old2_clipped = tf.boolean_mask(coord2_old_nn, tf.logical_not(outside_ind))
  
          coord1_vec = tf.boolean_mask(coord1_vec, tf.logical_not(outside_ind))
          coord2_vec = tf.boolean_mask(coord2_vec, tf.logical_not(outside_ind))
  
      coord_old_clipped = tf.cast(tf.transpose(tf.stack([coord_old1_clipped, coord_old2_clipped]), [1, 0]), tf.int32)
  
      # Coordinates of the new image
      coord_new = tf.transpose(tf.cast(tf.stack([coord1_vec, coord2_vec]), tf.int32), [1, 0])
  
      image_channel_list = tf.split(image, s[2], 2)
  
      image_rotated_channel_list = list()
      for image_channel in image_channel_list:
          image_chan_new_values = tf.gather_nd(tf.squeeze(image_channel), coord_old_clipped)
  
          if (mode == 'black') or (mode == 'repeat'):
              background_color = 0
          elif mode == 'ones':
              background_color = 1
          elif mode == 'white':
              background_color = 255
  
          image_rotated_channel_list.append(tf.sparse_to_dense(coord_new, [s[0], s[1]], image_chan_new_values,
                                                               background_color, validate_indices=False))
  
      image_rotated = tf.transpose(tf.stack(image_rotated_channel_list), [1, 2, 0])
  
      return image_rotated
  

Answer 8

Because I wanted to be able to rotate tensors I came up with the following piece of code, which rotates a [height, width, depth] tensor by a given angle:

def rotate_image_tensor(image, angle, mode='black'):
    """
    Rotates a 3D tensor (HWD), which represents an image by given radian angle.

    New image has the same size as the input image.

    mode controls what happens to border pixels.
    mode = 'black' results in black bars (value 0 in unknown areas)
    mode = 'white' results in value 255 in unknown areas
    mode = 'ones' results in value 1 in unknown areas
    mode = 'repeat' keeps repeating the closest pixel known
    """
    s = image.get_shape().as_list()
    assert len(s) == 3, "Input needs to be 3D."
    assert (mode == 'repeat') or (mode == 'black') or (mode == 'white') or (mode == 'ones'), "Unknown boundary mode."
    image_center = [np.floor(x/2) for x in s]

    # Coordinates of new image
    coord1 = tf.range(s[0])
    coord2 = tf.range(s[1])

    # Create vectors of those coordinates in order to vectorize the image
    coord1_vec = tf.tile(coord1, [s[1]])

    coord2_vec_unordered = tf.tile(coord2, [s[0]])
    coord2_vec_unordered = tf.reshape(coord2_vec_unordered, [s[0], s[1]])
    coord2_vec = tf.reshape(tf.transpose(coord2_vec_unordered, [1, 0]), [-1])

    # center coordinates since rotation center is supposed to be in the image center
    coord1_vec_centered = coord1_vec - image_center[0]
    coord2_vec_centered = coord2_vec - image_center[1]

    coord_new_centered = tf.cast(tf.pack([coord1_vec_centered, coord2_vec_centered]), tf.float32)

    # Perform backward transformation of the image coordinates
    rot_mat_inv = tf.dynamic_stitch([[0], [1], [2], [3]], [tf.cos(angle), tf.sin(angle), -tf.sin(angle), tf.cos(angle)])
    rot_mat_inv = tf.reshape(rot_mat_inv, shape=[2, 2])
    coord_old_centered = tf.matmul(rot_mat_inv, coord_new_centered)

    # Find nearest neighbor in old image
    coord1_old_nn = tf.cast(tf.round(coord_old_centered[0, :] + image_center[0]), tf.int32)
    coord2_old_nn = tf.cast(tf.round(coord_old_centered[1, :] + image_center[1]), tf.int32)

    # Clip values to stay inside image coordinates
    if mode == 'repeat':
        coord_old1_clipped = tf.minimum(tf.maximum(coord1_old_nn, 0), s[0]-1)
        coord_old2_clipped = tf.minimum(tf.maximum(coord2_old_nn, 0), s[1]-1)
    else:
        outside_ind1 = tf.logical_or(tf.greater(coord1_old_nn, s[0]-1), tf.less(coord1_old_nn, 0))
        outside_ind2 = tf.logical_or(tf.greater(coord2_old_nn, s[1]-1), tf.less(coord2_old_nn, 0))
        outside_ind = tf.logical_or(outside_ind1, outside_ind2)

        coord_old1_clipped = tf.boolean_mask(coord1_old_nn, tf.logical_not(outside_ind))
        coord_old2_clipped = tf.boolean_mask(coord2_old_nn, tf.logical_not(outside_ind))

        coord1_vec = tf.boolean_mask(coord1_vec, tf.logical_not(outside_ind))
        coord2_vec = tf.boolean_mask(coord2_vec, tf.logical_not(outside_ind))

    coord_old_clipped = tf.cast(tf.transpose(tf.pack([coord_old1_clipped, coord_old2_clipped]), [1, 0]), tf.int32)

    # Coordinates of the new image
    coord_new = tf.transpose(tf.cast(tf.pack([coord1_vec, coord2_vec]), tf.int32), [1, 0])

    image_channel_list = tf.split(2, s[2], image)

    image_rotated_channel_list = list()
    for image_channel in image_channel_list:
        image_chan_new_values = tf.gather_nd(tf.squeeze(image_channel), coord_old_clipped)

        if (mode == 'black') or (mode == 'repeat'):
            background_color = 0
        elif mode == 'ones':
            background_color = 1
        elif mode == 'white':
            background_color = 255

        image_rotated_channel_list.append(tf.sparse_to_dense(coord_new, [s[0], s[1]], image_chan_new_values,
                                                             background_color, validate_indices=False))

    image_rotated = tf.transpose(tf.pack(image_rotated_channel_list), [1, 2, 0])

    return image_rotated